Practical considerations in Bayesian fusion of point sensors

NASA Astrophysics Data System (ADS)

Johnson, Kevin; Minor, Christian

2012-06-01

Sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented.

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was

Improved blood glucose estimation through multi-sensor fusion.

PubMed

Xiong, Feiyu; Hipszer, Brian R; Joseph, Jeffrey; Kam, Moshe

2011-01-01

Continuous glucose monitoring systems are an integral component of diabetes management. Efforts to improve the accuracy and robustness of these systems are at the forefront of diabetes research. Towards this goal, a multi-sensor approach was evaluated in hospitalized patients. In this paper, we report on a multi-sensor fusion algorithm to combine glucose sensor measurements in a retrospective fashion. The results demonstrate the algorithm's ability to improve the accuracy and robustness of the blood glucose estimation with current glucose sensor technology.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

Frequency domain surface EMG sensor fusion for estimating finger forces.

PubMed

Potluri, Chandrasekhar; Kumar, Parmod; Anugolu, Madhavi; Urfer, Alex; Chiu, Steve; Naidu, D; Schoen, Marco P

2010-01-01

Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

Distributed multimodal data fusion for large scale wireless sensor networks

NASA Astrophysics Data System (ADS)

Ertin, Emre

2006-05-01

Sensor network technology has enabled new surveillance systems where sensor nodes equipped with processing and communication capabilities can collaboratively detect, classify and track targets of interest over a large surveillance area. In this paper we study distributed fusion of multimodal sensor data for extracting target information from a large scale sensor network. Optimal tracking, classification, and reporting of threat events require joint consideration of multiple sensor modalities. Multiple sensor modalities improve tracking by reducing the uncertainty in the track estimates as well as resolving track-sensor data association problems. Our approach to solving the fusion problem with large number of multimodal sensors is construction of likelihood maps. The likelihood maps provide a summary data for the solution of the detection, tracking and classification problem. The likelihood map presents the sensory information in an easy format for the decision makers to interpret and is suitable with fusion of spatial prior information such as maps, imaging data from stand-off imaging sensors. We follow a statistical approach to combine sensor data at different levels of uncertainty and resolution. The likelihood map transforms each sensor data stream to a spatio-temporal likelihood map ideally suitable for fusion with imaging sensor outputs and prior geographic information about the scene. We also discuss distributed computation of the likelihood map using a gossip based algorithm and present simulation results.

Generic Sensor Data Fusion Services for Web-enabled Environmental Risk Management and Decision-Support Systems

NASA Astrophysics Data System (ADS)

Sabeur, Zoheir; Middleton, Stuart; Veres, Galina; Zlatev, Zlatko; Salvo, Nicola

2010-05-01

The advancement of smart sensor technology in the last few years has led to an increase in the deployment of affordable sensors for monitoring the environment around Europe. This is generating large amounts of sensor observation information and inevitably leading to problems about how to manage large volumes of data as well as making sense out the data for decision-making. In addition, the various European Directives (Water Framework Diectives, Bathing Water Directives, Habitat Directives, etc.. ) which regulate human activities in the environment and the INSPIRE Directive on spatial information management regulations have implicitely led the designated European Member States environment agencies and authorities to put in place new sensor monitoring infrastructure and share information about environmental regions under their statutory responsibilities. They will need to work cross border and collectively reach environmental quality standards. They will also need to regularly report to the EC on the quality of the environments of which they are responsible and make such information accessible to the members of the public. In recent years, early pioneering work on the design of service oriented architecture using sensor networks has been achieved. Information web-services infrastructure using existing data catalogues and web-GIS map services can now be enriched with the deployment of new sensor observation and data fusion and modelling services using OGC standards. The deployment of the new services which describe sensor observations and intelligent data-processing using data fusion techniques can now be implemented and provide added value information with spatial-temporal uncertainties to the next generation of decision support service systems. The new decision support service systems have become key to implement across Europe in order to comply with EU environmental regulations and INSPIRE. In this paper, data fusion services using OGC standards with sensor

An Approach to Automated Fusion System Design and Adaptation

PubMed Central

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-01-01

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum. PMID:28300762

An Approach to Automated Fusion System Design and Adaptation.

PubMed

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-03-16

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

PubMed Central

Mendes, José Jair Alves; Vieira, Mário Elias Marinho; Pires, Marcelo Bissi; Stevan, Sergio Luiz

2016-01-01

The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others. PMID:27669260

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications.

PubMed

Mendes, José Jair Alves; Vieira, Mário Elias Marinho; Pires, Marcelo Bissi; Stevan, Sergio Luiz

2016-09-23

The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others.

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter.

PubMed

Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei

2016-11-02

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system's error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

PubMed Central

Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei

2016-01-01

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph.

PubMed

Zeng, Qinghua; Chen, Weina; Liu, Jianye; Wang, Huizhe

2017-03-21

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method.

Study on the multi-sensors monitoring and information fusion technology of dangerous cargo container

NASA Astrophysics Data System (ADS)

Xu, Shibo; Zhang, Shuhui; Cao, Wensheng

2017-10-01

In this paper, monitoring system of dangerous cargo container based on multi-sensors is presented. In order to improve monitoring accuracy, multi-sensors will be applied inside of dangerous cargo container. Multi-sensors information fusion solution of monitoring dangerous cargo container is put forward, and information pre-processing, the fusion algorithm of homogenous sensors and information fusion based on BP neural network are illustrated, applying multi-sensors in the field of container monitoring has some novelty.

Sensor fusion for antipersonnel landmine detection: a case study

NASA Astrophysics Data System (ADS)

den Breejen, Eric; Schutte, Klamer; Cremer, Frank

1999-08-01

In this paper the multi sensor fusion results obtained within the European research project GEODE are presented. The layout of the test lane and the individual sensors used are described. The implementation of the SCOOP algorithm improves the ROC curves, as the false alarm surface and the number of false alarms both are taken into account. The confidence grids, as produced by the sensor manufacturers, of the sensors are used as input for the different sensor fusion methods implemented. The multisensor fusion methods implemented are Bayes, Dempster-Shafer, fuzzy probabilities and rules. The mapping of the confidence grids to the input parameters for fusion methods is an important step. Due to limited amount of the available data the entire test lane is used for training and evaluation. All four sensor fusion methods provide better detection results than the individual sensors.

Integrative Multi-Spectral Sensor Device for Far-Infrared and Visible Light Fusion

NASA Astrophysics Data System (ADS)

Qiao, Tiezhu; Chen, Lulu; Pang, Yusong; Yan, Gaowei

2018-06-01

Infrared and visible light image fusion technology is a hot spot in the research of multi-sensor fusion technology in recent years. Existing infrared and visible light fusion technologies need to register before fusion because of using two cameras. However, the application effect of the registration technology has yet to be improved. Hence, a novel integrative multi-spectral sensor device is proposed for infrared and visible light fusion, and by using the beam splitter prism, the coaxial light incident from the same lens is projected to the infrared charge coupled device (CCD) and visible light CCD, respectively. In this paper, the imaging mechanism of the proposed sensor device is studied with the process of the signals acquisition and fusion. The simulation experiment, which involves the entire process of the optic system, signal acquisition, and signal fusion, is constructed based on imaging effect model. Additionally, the quality evaluation index is adopted to analyze the simulation result. The experimental results demonstrate that the proposed sensor device is effective and feasible.

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph

PubMed Central

Zeng, Qinghua; Chen, Weina; Liu, Jianye; Wang, Huizhe

2017-01-01

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method. PMID:28335570

Sensor fusion: lane marking detection and autonomous intelligent cruise control system

NASA Astrophysics Data System (ADS)

Baret, Marc; Baillarin, S.; Calesse, C.; Martin, Lionel

1995-12-01

In the past few years MATRA and RENAULT have developed an Autonomous Intelligent Cruise Control (AICC) system based on a LIDAR sensor. This sensor incorporating a charge coupled device was designed to acquire pulsed laser diode emission reflected by standard car reflectors. The absence of moving mechanical parts, the large field of view, the high measurement rate and the very good accuracy for distance range and angular position of targets make this sensor very interesting. It provides the equipped car with the distance and the relative speed of other vehicles enabling the safety distance to be controlled by acting on the throttle and the automatic gear box. Experiments in various real traffic situations have shown the limitations of this kind of system especially on bends. All AICC sensors are unable to distinguish between a bend and a change of lane. This is easily understood if we consider a road without lane markings. This fact has led MATRA to improve its AICC system by providing the lane marking information. Also in the scope of the EUREKA PROMETHEUS project, MATRA and RENAULT have developed a lane keeping system in order to warn of the drivers lack of vigilance. Thus, MATRA have spread this system to far field lane marking detection and have coupled it with the AICC system. Experiments will be carried out on roads to estimate the gain in performance and comfort due to this fusion.

Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring.

PubMed

Hoog Antink, Christoph; Schulz, Florian; Leonhardt, Steffen; Walter, Marian

2017-12-25

Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR S is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.

All-IP-Ethernet architecture for real-time sensor-fusion processing

NASA Astrophysics Data System (ADS)

Hiraki, Kei; Inaba, Mary; Tezuka, Hiroshi; Tomari, Hisanobu; Koizumi, Kenichi; Kondo, Shuya

2016-03-01

Serendipter is a device that distinguishes and selects very rare particles and cells from huge amount of population. We are currently designing and constructing information processing system for a Serendipter. The information processing system for Serendipter is a kind of sensor-fusion system but with much more difficulties: To fulfill these requirements, we adopt All IP based architecture: All IP-Ethernet based data processing system consists of (1) sensor/detector directly output data as IP-Ethernet packet stream, (2) single Ethernet/TCP/IP streams by a L2 100Gbps Ethernet switch, (3) An FPGA board with 100Gbps Ethernet I/F connected to the switch and a Xeon based server. Circuits in the FPGA include 100Gbps Ethernet MAC, buffers and preprocessing, and real-time Deep learning circuits using multi-layer neural networks. Proposed All-IP architecture solves existing problem to construct large-scale sensor-fusion systems.

Non-verbal communication through sensor fusion

NASA Astrophysics Data System (ADS)

Tairych, Andreas; Xu, Daniel; O'Brien, Benjamin M.; Anderson, Iain A.

2016-04-01

When we communicate face to face, we subconsciously engage our whole body to convey our message. In telecommunication, e.g. during phone calls, this powerful information channel cannot be used. Capturing nonverbal information from body motion and transmitting it to the receiver parallel to speech would make these conversations feel much more natural. This requires a sensing device that is capable of capturing different types of movements, such as the flexion and extension of joints, and the rotation of limbs. In a first embodiment, we developed a sensing glove that is used to control a computer game. Capacitive dielectric elastomer (DE) sensors measure finger positions, and an inertial measurement unit (IMU) detects hand roll. These two sensor technologies complement each other, with the IMU allowing the player to move an avatar through a three-dimensional maze, and the DE sensors detecting finger flexion to fire weapons or open doors. After demonstrating the potential of sensor fusion in human-computer interaction, we take this concept to the next level and apply it in nonverbal communication between humans. The current fingerspelling glove prototype uses capacitive DE sensors to detect finger gestures performed by the sending person. These gestures are mapped to corresponding messages and transmitted wirelessly to another person. A concept for integrating an IMU into this system is presented. The fusion of the DE sensor and the IMU combines the strengths of both sensor types, and therefore enables very comprehensive body motion sensing, which makes a large repertoire of gestures available to nonverbal communication over distances.

Advances in multi-sensor data fusion: algorithms and applications.

PubMed

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

Sensor fusion approaches for EMI and GPR-based subsurface threat identification

NASA Astrophysics Data System (ADS)

Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

2011-06-01

Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of pattern recognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that nonlinear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.

Sensor fusion for synthetic vision

NASA Technical Reports Server (NTRS)

Pavel, M.; Larimer, J.; Ahumada, A.

1991-01-01

Display methodologies are explored for fusing images gathered by millimeter wave sensors with images rendered from an on-board terrain data base to facilitate visually guided flight and ground operations in low visibility conditions. An approach to fusion based on multiresolution image representation and processing is described which facilitates fusion of images differing in resolution within and between images. To investigate possible fusion methods, a workstation-based simulation environment is being developed.

Hand-writing motion tracking with vision-inertial sensor fusion: calibration and error correction.

PubMed

Zhou, Shengli; Fei, Fei; Zhang, Guanglie; Liu, Yunhui; Li, Wen J

2014-08-25

The purpose of this study was to improve the accuracy of real-time ego-motion tracking through inertial sensor and vision sensor fusion. Due to low sampling rates supported by web-based vision sensor and accumulation of errors in inertial sensors, ego-motion tracking with vision sensors is commonly afflicted by slow updating rates, while motion tracking with inertial sensor suffers from rapid deterioration in accuracy with time. This paper starts with a discussion of developed algorithms for calibrating two relative rotations of the system using only one reference image. Next, stochastic noises associated with the inertial sensor are identified using Allan Variance analysis, and modeled according to their characteristics. Finally, the proposed models are incorporated into an extended Kalman filter for inertial sensor and vision sensor fusion. Compared with results from conventional sensor fusion models, we have shown that ego-motion tracking can be greatly enhanced using the proposed error correction model.

Hand-Writing Motion Tracking with Vision-Inertial Sensor Fusion: Calibration and Error Correction

PubMed Central

Zhou, Shengli; Fei, Fei; Zhang, Guanglie; Liu, Yunhui; Li, Wen J.

2014-01-01

The purpose of this study was to improve the accuracy of real-time ego-motion tracking through inertial sensor and vision sensor fusion. Due to low sampling rates supported by web-based vision sensor and accumulation of errors in inertial sensors, ego-motion tracking with vision sensors is commonly afflicted by slow updating rates, while motion tracking with inertial sensor suffers from rapid deterioration in accuracy with time. This paper starts with a discussion of developed algorithms for calibrating two relative rotations of the system using only one reference image. Next, stochastic noises associated with the inertial sensor are identified using Allan Variance analysis, and modeled according to their characteristics. Finally, the proposed models are incorporated into an extended Kalman filter for inertial sensor and vision sensor fusion. Compared with results from conventional sensor fusion models, we have shown that ego-motion tracking can be greatly enhanced using the proposed error correction model. PMID:25157546

Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy. PMID:25608211

Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

PubMed Central

Jiang, Wen; Xie, Chunhe; Zhuang, Miaoyan; Shou, Yehang; Tang, Yongchuan

2016-01-01

Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster–Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection. PMID:27649193

Sensor fusion III: 3-D perception and recognition; Proceedings of the Meeting, Boston, MA, Nov. 5-8, 1990

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1991-01-01

The volume on data fusion from multiple sources discusses fusing multiple views, temporal analysis and 3D motion interpretation, sensor fusion and eye-to-hand coordination, and integration in human shape perception. Attention is given to surface reconstruction, statistical methods in sensor fusion, fusing sensor data with environmental knowledge, computational models for sensor fusion, and evaluation and selection of sensor fusion techniques. Topics addressed include the structure of a scene from two and three projections, optical flow techniques for moving target detection, tactical sensor-based exploration in a robotic environment, and the fusion of human and machine skills for remote robotic operations. Also discussed are K-nearest-neighbor concepts for sensor fusion, surface reconstruction with discontinuities, a sensor-knowledge-command fusion paradigm for man-machine systems, coordinating sensing and local navigation, and terrain map matching using multisensing techniques for applications to autonomous vehicle navigation.

A Radiosonde Using a Humidity Sensor Array with a Platinum Resistance Heater and Multi-Sensor Data Fusion

PubMed Central

Shi, Yunbo; Luo, Yi; Zhao, Wenjie; Shang, Chunxue; Wang, Yadong; Chen, Yinsheng

2013-01-01

This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes. PMID:23857263

A radiosonde using a humidity sensor array with a platinum resistance heater and multi-sensor data fusion.

PubMed

Shi, Yunbo; Luo, Yi; Zhao, Wenjie; Shang, Chunxue; Wang, Yadong; Chen, Yinsheng

2013-07-12

This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes.

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

PubMed Central

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-01-01

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes. PMID:26712766

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation.

PubMed

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-12-26

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot's wheels, and 24 fuzzy rules for the robot's movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

Context-Aided Sensor Fusion for Enhanced Urban Navigation

PubMed Central

Martí, Enrique David; Martín, David; García, Jesús; de la Escalera, Arturo; Molina, José Manuel; Armingol, José María

2012-01-01

The deployment of Intelligent Vehicles in urban environments requires reliable estimation of positioning for urban navigation. The inherent complexity of this kind of environments fosters the development of novel systems which should provide reliable and precise solutions to the vehicle. This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions. The constrained non-linear filter is here conditioned by a contextual knowledge module which reasons about sensor quality and driving context in order to adapt it to the situation, while at the same time it carries out a continuous estimation and correction of INS drift errors. An exhaustive analysis has been carried out with available data in order to characterize the behavior of available sensors and take it into account in the developed solution. The performance is then analyzed with an extensive dataset containing representative situations. The proposed solution suits the use of fusion algorithms for deploying Intelligent Transport Systems in urban environments. PMID:23223080

Context-aided sensor fusion for enhanced urban navigation.

PubMed

Martí, Enrique David; Martín, David; García, Jesús; de la Escalera, Arturo; Molina, José Manuel; Armingol, José María

2012-12-06

 The deployment of Intelligent Vehicles in urban environments requires reliable estimation of positioning for urban navigation. The inherent complexity of this kind of environments fosters the development of novel systems which should provide reliable and precise solutions to the vehicle. This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions. The constrained non-linear filter is here conditioned by a contextual knowledge module which reasons about sensor quality and driving context in order to adapt it to the situation, while at the same time it carries out a continuous estimation and correction of INS drift errors. An exhaustive analysis has been carried out with available data in order to characterize the behavior of available sensors and take it into account in the developed solution. The performance is then analyzed with an extensive dataset containing representative situations. The proposed solution suits the use of fusion algorithms for deploying Intelligent Transport Systems in urban environments.

A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators.

PubMed

Ligorio, Gabriele; Sabatini, Angelo Maria

2015-12-19

In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented.

Deep data fusion method for missile-borne inertial/celestial system

NASA Astrophysics Data System (ADS)

Zhang, Chunxi; Chen, Xiaofei; Lu, Jiazhen; Zhang, Hao

2018-05-01

Strap-down inertial-celestial integrated navigation system has the advantages of autonomy and high precision and is very useful for ballistic missiles. The star sensor installation error and inertial measurement error have a great influence for the system performance. Based on deep data fusion, this paper establishes measurement equations including star sensor installation error and proposes the deep fusion filter method. Simulations including misalignment error, star sensor installation error, IMU error are analyzed. Simulation results indicate that the deep fusion method can estimate the star sensor installation error and IMU error. Meanwhile, the method can restrain the misalignment errors caused by instrument errors.

A near-optimal low complexity sensor fusion technique for accurate indoor localization based on ultrasound time of arrival measurements from low-quality sensors

NASA Astrophysics Data System (ADS)

Mitilineos, Stelios A.; Argyreas, Nick D.; Thomopoulos, Stelios C. A.

2009-05-01

A fusion-based localization technique for location-based services in indoor environments is introduced herein, based on ultrasound time-of-arrival measurements from multiple off-the-shelf range estimating sensors which are used in a market-available localization system. In-situ field measurements results indicated that the respective off-the-shelf system was unable to estimate position in most of the cases, while the underlying sensors are of low-quality and yield highly inaccurate range and position estimates. An extensive analysis is performed and a model of the sensor-performance characteristics is established. A low-complexity but accurate sensor fusion and localization technique is then developed, which consists inof evaluating multiple sensor measurements and selecting the one that is considered most-accurate based on the underlying sensor model. Optimality, in the sense of a genie selecting the optimum sensor, is subsequently evaluated and compared to the proposed technique. The experimental results indicate that the proposed fusion method exhibits near-optimal performance and, albeit being theoretically suboptimal, it largely overcomes most flaws of the underlying single-sensor system resulting in a localization system of increased accuracy, robustness and availability.

A flexible data fusion architecture for persistent surveillance using ultra-low-power wireless sensor networks

NASA Astrophysics Data System (ADS)

Hanson, Jeffrey A.; McLaughlin, Keith L.; Sereno, Thomas J.

2011-06-01

We have developed a flexible, target-driven, multi-modal, physics-based fusion architecture that efficiently searches sensor detections for targets and rejects clutter while controlling the combinatoric problems that commonly arise in datadriven fusion systems. The informational constraints imposed by long lifetime requirements make systems vulnerable to false alarms. We demonstrate that our data fusion system significantly reduces false alarms while maintaining high sensitivity to threats. In addition, mission goals can vary substantially in terms of targets-of-interest, required characterization, acceptable latency, and false alarm rates. Our fusion architecture provides the flexibility to match these trade-offs with mission requirements unlike many conventional systems that require significant modifications for each new mission. We illustrate our data fusion performance with case studies that span many of the potential mission scenarios including border surveillance, base security, and infrastructure protection. In these studies, we deployed multi-modal sensor nodes - including geophones, magnetometers, accelerometers and PIR sensors - with low-power processing algorithms and low-bandwidth wireless mesh networking to create networks capable of multi-year operation. The results show our data fusion architecture maintains high sensitivities while suppressing most false alarms for a variety of environments and targets.

Design of a multisensor data fusion system for target detection

NASA Astrophysics Data System (ADS)

Thomopoulos, Stelios C.; Okello, Nickens N.; Kadar, Ivan; Lovas, Louis A.

1993-09-01

The objective of this paper is to discuss the issues that are involved in the design of a multisensor fusion system and provide a systematic analysis and synthesis methodology for the design of the fusion system. The system under consideration consists of multifrequency (similar) radar sensors. However, the fusion design must be flexible to accommodate additional dissimilar sensors such as IR, EO, ESM, and Ladar. The motivation for the system design is the proof of the fusion concept for enhancing the detectability of small targets in clutter. In the context of down-selecting the proper configuration for multisensor (similar and dissimilar, and centralized vs. distributed) data fusion, the issues of data modeling, fusion approaches, and fusion architectures need to be addressed for the particular application being considered. Although the study of different approaches may proceed in parallel, the interplay among them is crucial in selecting a fusion configuration for a given application. The natural sequence for addressing the three different issues is to begin from the data modeling, in order to determine the information content of the data. This information will dictate the appropriate fusion approach. This, in turn, will lead to a global fusion architecture. Both distributed and centralized fusion architectures are used to illustrate the design issues along with Monte-Carlo simulation performance comparison of a single sensor versus a multisensor centrally fused system.

Desensitized Optimal Filtering and Sensor Fusion Toolkit

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.

2015-01-01

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

Optimal Sensor Fusion for Structural Health Monitoring of Aircraft Composite Components

DTIC Science & Technology

2011-09-01

sensor networks combine or fuse different types of sensors. Fiber Bragg Grating ( FBG ) sensors can be inserted in layers of composite structures to...consideration. This paper describes an example of optimal sensor fusion, which combines FBG sensors and PZT sensors. Optimal sensor fusion tries to find...Fiber Bragg Grating ( FBG ) sensors can be inserted in layers of composite structures to provide local damage detection, while surface mounted

Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking.

PubMed

Ligorio, Gabriele; Bergamini, Elena; Pasciuto, Ilaria; Vannozzi, Giuseppe; Cappozzo, Aurelio; Sabatini, Angelo Maria

2016-01-26

Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor's uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process.

Evaluation of taste solutions by sensor fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kojima, Yohichiro; Sato, Eriko; Atobe, Masahiko

In our previous studies, properties of taste solutions were discriminated based on sound velocity and amplitude of ultrasonic waves propagating through the solutions. However, to make this method applicable to beverages which contain many taste substances, further studies are required. In this study, the waveform of an ultrasonic wave with frequency of approximately 5 MHz propagating through a solution was measured and subjected to frequency analysis. Further, taste sensors require various techniques of sensor fusion to effectively obtain chemical and physical parameter of taste solutions. A sensor fusion method of ultrasonic wave sensor and various sensors, such as the surfacemore » plasmon resonance (SPR) sensor, to estimate tastes were proposed and examined in this report. As a result, differences among pure water and two basic taste solutions were clearly observed as differences in their properties. Furthermore, a self-organizing neural network was applied to obtained data which were used to clarify the differences among solutions.« less

Miniature fiber Fabry-Perot sensors based on fusion splicing

NASA Astrophysics Data System (ADS)

Zhu, Jia-li; Wang, Ming; Yang, Chun-di; Wang, Ting-ting

2013-03-01

Fiber-optic Fabry-Perot (F-P) sensors are widely investigated because they have several advantages over conventional sensors, such as immunity to electromagnetic interference, ability to operate under bad environments, high sensitivity and the potential for multiplexing. A new method to fabricate micro-cavity Fabry-Perot interferometer is introduced, which is fusion splicing a section of conventional single-mode fiber (SMF) and a section of hollow core or solid core photonic crystal fiber (PCF) together to form a micro-cavity at the splice joint. The technology of fusion splicing is discussed, and two miniature optical fiber sensors based on Fabry-Perot interference using fusion splicing are presented. The two sensors are completely made of fused silica, and have good high-temperature capability.

Real-time Enhancement, Registration, and Fusion for a Multi-Sensor Enhanced Vision System

NASA Technical Reports Server (NTRS)

Hines, Glenn D.; Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.

2006-01-01

Over the last few years NASA Langley Research Center (LaRC) has been developing an Enhanced Vision System (EVS) to aid pilots while flying in poor visibility conditions. The EVS captures imagery using two infrared video cameras. The cameras are placed in an enclosure that is mounted and flown forward-looking underneath the NASA LaRC ARIES 757 aircraft. The data streams from the cameras are processed in real-time and displayed on monitors on-board the aircraft. With proper processing the camera system can provide better-than- human-observed imagery particularly during poor visibility conditions. However, to obtain this goal requires several different stages of processing including enhancement, registration, and fusion, and specialized processing hardware for real-time performance. We are using a real-time implementation of the Retinex algorithm for image enhancement, affine transformations for registration, and weighted sums to perform fusion. All of the algorithms are executed on a single TI DM642 digital signal processor (DSP) clocked at 720 MHz. The image processing components were added to the EVS system, tested, and demonstrated during flight tests in August and September of 2005. In this paper we briefly discuss the EVS image processing hardware and algorithms. We then discuss implementation issues and show examples of the results obtained during flight tests. Keywords: enhanced vision system, image enhancement, retinex, digital signal processing, sensor fusion

Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

PubMed Central

Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

2013-01-01

Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study. PMID:24351636

Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

PubMed

Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

2013-12-13

Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

Distributed Sensor Fusion for Scalar Field Mapping Using Mobile Sensor Networks.

PubMed

La, Hung Manh; Sheng, Weihua

2013-04-01

In this paper, autonomous mobile sensor networks are deployed to measure a scalar field and build its map. We develop a novel method for multiple mobile sensor nodes to build this map using noisy sensor measurements. Our method consists of two parts. First, we develop a distributed sensor fusion algorithm by integrating two different distributed consensus filters to achieve cooperative sensing among sensor nodes. This fusion algorithm has two phases. In the first phase, the weighted average consensus filter is developed, which allows each sensor node to find an estimate of the value of the scalar field at each time step. In the second phase, the average consensus filter is used to allow each sensor node to find a confidence of the estimate at each time step. The final estimate of the value of the scalar field is iteratively updated during the movement of the mobile sensors via weighted average. Second, we develop the distributed flocking-control algorithm to drive the mobile sensors to form a network and track the virtual leader moving along the field when only a small subset of the mobile sensors know the information of the leader. Experimental results are provided to demonstrate our proposed algorithms.

Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking

PubMed Central

Ligorio, Gabriele; Bergamini, Elena; Pasciuto, Ilaria; Vannozzi, Giuseppe; Cappozzo, Aurelio; Sabatini, Angelo Maria

2016-01-01

Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor’s uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process. PMID:26821027

Advances in Multi-Sensor Information Fusion: Theory and Applications 2017.

PubMed

Jin, Xue-Bo; Sun, Shuli; Wei, Hong; Yang, Feng-Bao

2018-04-11

The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

PubMed Central

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered. PMID:22969386

On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition.

PubMed

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

Conflict management based on belief function entropy in sensor fusion.

PubMed

Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

2016-01-01

Wireless sensor network plays an important role in intelligent navigation. It incorporates a group of sensors to overcome the limitation of single detection system. Dempster-Shafer evidence theory can combine the sensor data of the wireless sensor network by data fusion, which contributes to the improvement of accuracy and reliability of the detection system. However, due to different sources of sensors, there may be conflict among the sensor data under uncertain environment. Thus, this paper proposes a new method combining Deng entropy and evidence distance to address the issue. First, Deng entropy is adopted to measure the uncertain information. Then, evidence distance is applied to measure the conflict degree. The new method can cope with conflict effectually and improve the accuracy and reliability of the detection system. An example is illustrated to show the efficiency of the new method and the result is compared with that of the existing methods.

An epidemic model for biological data fusion in ad hoc sensor networks

NASA Astrophysics Data System (ADS)

Chang, K. C.; Kotari, Vikas

2009-05-01

Bio terrorism can be a very refined and a catastrophic approach of attacking a nation. This requires the development of a complete architecture dedicatedly designed for this purpose which includes but is not limited to Sensing/Detection, Tracking and Fusion, Communication, and others. In this paper we focus on one such architecture and evaluate its performance. Various sensors for this specific purpose have been studied. The accent has been on use of Distributed systems such as ad-hoc networks and on application of epidemic data fusion algorithms to better manage the bio threat data. The emphasis has been on understanding the performance characteristics of these algorithms under diversified real time scenarios which are implemented through extensive JAVA based simulations. Through comparative studies on communication and fusion the performance of channel filter algorithm for the purpose of biological sensor data fusion are validated.

A Motion Tracking and Sensor Fusion Module for Medical Simulation.

PubMed

Shen, Yunhe; Wu, Fan; Tseng, Kuo-Shih; Ye, Ding; Raymond, John; Konety, Badrinath; Sweet, Robert

2016-01-01

Here we introduce a motion tracking or navigation module for medical simulation systems. Our main contribution is a sensor fusion method for proximity or distance sensors integrated with inertial measurement unit (IMU). Since IMU rotation tracking has been widely studied, we focus on the position or trajectory tracking of the instrument moving freely within a given boundary. In our experiments, we have found that this module reliably tracks instrument motion.

A New Multi-Sensor Track Fusion Architecture for Multi-Sensor Information Integration

DTIC Science & Technology

2004-09-01

NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION ...NAME(S) AND ADDRESS(ES) Lockheed Martin Aeronautical Systems Company,Marietta,GA,3063 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING...tracking process and degrades the track accuracy. ARCHITECHTURE OF MULTI-SENSOR TRACK FUSION MODEL The Alpha

Multi-Sensor Systems and Data Fusion for Telecommunications, Remote Sensing and Radar (les Systemes multi-senseurs et le fusionnement des donnees pour les telecommunications, la teledetection et les radars)

DTIC Science & Technology

1998-04-01

The result of the project is a demonstration of the fusion process, the sensors management and the real-time capabilities using simulated sensors...demonstrator (TAD) is a system that demonstrates the core ele- ment of a battlefield ground surveillance system by simulation in near real-time. The core...Management and Sensor/Platform simulation . The surveillance system observes the real world through a non-collocated heterogene- ous multisensory system

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang

2015-01-01

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule. PMID:26295399

Cognitive foundations for model-based sensor fusion

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Weijers, Bertus; Mutz, Chris W.

2003-08-01

Target detection, tracking, and sensor fusion are complicated problems, which usually are performed sequentially. First detecting targets, then tracking, then fusing multiple sensors reduces computations. This procedure however is inapplicable to difficult targets which cannot be reliably detected using individual sensors, on individual scans or frames. In such more complicated cases one has to perform functions of fusing, tracking, and detecting concurrently. This often has led to prohibitive combinatorial complexity and, as a consequence, to sub-optimal performance as compared to the information-theoretic content of all the available data. It is well appreciated that in this task the human mind is by far superior qualitatively to existing mathematical methods of sensor fusion, however, the human mind is limited in the amount of information and speed of computation it can cope with. Therefore, research efforts have been devoted toward incorporating "biological lessons" into smart algorithms, yet success has been limited. Why is this so, and how to overcome existing limitations? The fundamental reasons for current limitations are analyzed and a potentially breakthrough research and development effort is outlined. We utilize the way our mind combines emotions and concepts in the thinking process and present the mathematical approach to accomplishing this in the current technology computers. The presentation will summarize the difficulties encountered by intelligent systems over the last 50 years related to combinatorial complexity, analyze the fundamental limitations of existing algorithms and neural networks, and relate it to the type of logic underlying the computational structure: formal, multivalued, and fuzzy logic. A new concept of dynamic logic will be introduced along with algorithms capable of pulling together all the available information from multiple sources. This new mathematical technique, like our brain, combines conceptual understanding with

Sensor data monitoring and decision level fusion scheme for early fire detection

NASA Astrophysics Data System (ADS)

Rizogiannis, Constantinos; Thanos, Konstantinos Georgios; Astyakopoulos, Alkiviadis; Kyriazanos, Dimitris M.; Thomopoulos, Stelios C. A.

2017-05-01

The aim of this paper is to present the sensor monitoring and decision level fusion scheme for early fire detection which has been developed in the context of the AF3 Advanced Forest Fire Fighting European FP7 research project, adopted specifically in the OCULUS-Fire control and command system and tested during a firefighting field test in Greece with prescribed real fire, generating early-warning detection alerts and notifications. For this purpose and in order to improve the reliability of the fire detection system, a two-level fusion scheme is developed exploiting a variety of observation solutions from air e.g. UAV infrared cameras, ground e.g. meteorological and atmospheric sensors and ancillary sources e.g. public information channels, citizens smartphone applications and social media. In the first level, a change point detection technique is applied to detect changes in the mean value of each measured parameter by the ground sensors such as temperature, humidity and CO2 and then the Rate-of-Rise of each changed parameter is calculated. In the second level the fire event Basic Probability Assignment (BPA) function is determined for each ground sensor using Fuzzy-logic theory and then the corresponding mass values are combined in a decision level fusion process using Evidential Reasoning theory to estimate the final fire event probability.

Energy Logic (EL): a novel fusion engine of multi-modality multi-agent data/information fusion for intelligent surveillance systems

NASA Astrophysics Data System (ADS)

Rababaah, Haroun; Shirkhodaie, Amir

2009-04-01

The rapidly advancing hardware technology, smart sensors and sensor networks are advancing environment sensing. One major potential of this technology is Large-Scale Surveillance Systems (LS3) especially for, homeland security, battlefield intelligence, facility guarding and other civilian applications. The efficient and effective deployment of LS3 requires addressing number of aspects impacting the scalability of such systems. The scalability factors are related to: computation and memory utilization efficiency, communication bandwidth utilization, network topology (e.g., centralized, ad-hoc, hierarchical or hybrid), network communication protocol and data routing schemes; and local and global data/information fusion scheme for situational awareness. Although, many models have been proposed to address one aspect or another of these issues but, few have addressed the need for a multi-modality multi-agent data/information fusion that has characteristics satisfying the requirements of current and future intelligent sensors and sensor networks. In this paper, we have presented a novel scalable fusion engine for multi-modality multi-agent information fusion for LS3. The new fusion engine is based on a concept we call: Energy Logic. Experimental results of this work as compared to a Fuzzy logic model strongly supported the validity of the new model and inspired future directions for different levels of fusion and different applications.

Sensor Fusion of Position- and Micro-Sensors (MEMS) integrated in a Wireless Sensor Network for movement detection in landslide areas

NASA Astrophysics Data System (ADS)

Arnhardt, Christian; Fernández-Steeger, Tomas; Azzam, Rafig

2010-05-01

Monitoring systems in landslide areas are important elements of effective Early Warning structures. Data acquisition and retrieval allows the detection of movement processes and thus is essential to generate warnings in time. Apart from the precise measurement, the reliability of data is fundamental, because outliers can trigger false alarms and leads to the loss of acceptance of such systems. For the monitoring of mass movements and their risk it is important to know, if there is movement, how fast it is and how trustworthy is the information. The joint project "Sensorbased landslide early warning system" (SLEWS) deals with these questions, and tries to improve data quality and to reduce false alarm rates, due to the combination of sensor date (sensor fusion). The project concentrates on the development of a prototypic Alarm- and Early Warning system (EWS) for different types of landslides by using various low-cost sensors, integrated in a wireless sensor network (WSN). The network consists of numerous connection points (nodes) that transfer data directly or over other nodes (Multi-Hop) in real-time to a data collection point (gateway). From there all the data packages are transmitted to a spatial data infrastructure (SDI) for further processing, analyzing and visualizing with respect to end-user specifications. The ad-hoc characteristic of the network allows the autonomous crosslinking of the nodes according to existing connections and communication strength. Due to the independent finding of new or more stable connections (self healing) a breakdown of the whole system is avoided. The bidirectional data stream enables the receiving of data from the network but also allows the transfer of commands and pointed requests into the WSN. For the detection of surface deformations in landslide areas small low-cost Micro-Electro-Mechanical-Systems (MEMS) and positionsensors from the automobile industries, different industrial applications and from other measurement

Sensor management in RADAR/IRST track fusion

NASA Astrophysics Data System (ADS)

Hu, Shi-qiang; Jing, Zhong-liang

2004-07-01

In this paper, a novel radar management strategy technique suitable for RADAR/IRST track fusion, which is based on Fisher Information Matrix (FIM) and fuzzy stochastic decision approach, is put forward. Firstly, optimal radar measurements' scheduling is obtained by the method of maximizing determinant of the Fisher information matrix of radar and IRST measurements, which is managed by the expert system. Then, suggested a "pseudo sensor" to predict the possible target position using the polynomial method based on the radar and IRST measurements, using "pseudo sensor" model to estimate the target position even if the radar is turned off. At last, based on the tracking performance and the state of target maneuver, fuzzy stochastic decision is used to adjust the optimal radar scheduling and retrieve the module parameter of "pseudo sensor". The experiment result indicates that the algorithm can not only limit Radar activity effectively but also keep the tracking accuracy of active/passive system well. And this algorithm eliminates the drawback of traditional Radar management methods that the Radar activity is fixed and not easy to control and protect.

Sensor fusion II: Human and machine strategies; Proceedings of the Meeting, Philadelphia, PA, Nov. 6-9, 1989

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1990-01-01

Various papers on human and machine strategies in sensor fusion are presented. The general topics addressed include: active vision, measurement and analysis of visual motion, decision models for sensor fusion, implementation of sensor fusion algorithms, applying sensor fusion to image analysis, perceptual modules and their fusion, perceptual organization and object recognition, planning and the integration of high-level knowledge with perception, using prior knowledge and context in sensor fusion.

Research on the strategy of underwater united detection fusion and communication using multi-sensor

NASA Astrophysics Data System (ADS)

Xu, Zhenhua; Huang, Jianguo; Huang, Hai; Zhang, Qunfei

2011-09-01

In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.

Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.

ELIPS: Toward a Sensor Fusion Processor on a Chip

NASA Technical Reports Server (NTRS)

Daud, Taher; Stoica, Adrian; Tyson, Thomas; Li, Wei-te; Fabunmi, James

1998-01-01

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the ELIPS concept targets interceptor functionality; other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data, three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy

PubMed Central

Cho, Taehwan; Lee, Changho; Choi, Sangbang

2013-01-01

The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS), Automatic Dependent Surveillance-Broadcast (ADS-B), multilateration (MLAT) and wide-area multilateration (WAM) systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM) filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter. PMID:23535715

Multi-sensor fusion with interacting multiple model filter for improved aircraft position accuracy.

PubMed

Cho, Taehwan; Lee, Changho; Choi, Sangbang

2013-03-27

The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS), Automatic Dependent Surveillance-Broadcast (ADS-B), multilateration (MLAT) and wide-area multilateration (WAM) systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM) filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.

A Data Fusion Method in Wireless Sensor Networks

PubMed Central

Izadi, Davood; Abawajy, Jemal H.; Ghanavati, Sara; Herawan, Tutut

2015-01-01

The success of a Wireless Sensor Network (WSN) deployment strongly depends on the quality of service (QoS) it provides regarding issues such as data accuracy, data aggregation delays and network lifetime maximisation. This is especially challenging in data fusion mechanisms, where a small fraction of low quality data in the fusion input may negatively impact the overall fusion result. In this paper, we present a fuzzy-based data fusion approach for WSN with the aim of increasing the QoS whilst reducing the energy consumption of the sensor network. The proposed approach is able to distinguish and aggregate only true values of the collected data as such, thus reducing the burden of processing the entire data at the base station (BS). It is also able to eliminate redundant data and consequently reduce energy consumption thus increasing the network lifetime. We studied the effectiveness of the proposed data fusion approach experimentally and compared it with two baseline approaches in terms of data collection, number of transferred data packets and energy consumption. The results of the experiments show that the proposed approach achieves better results than the baseline approaches. PMID:25635417

Sensor and information fusion for improved hostile fire situational awareness

NASA Astrophysics Data System (ADS)

Scanlon, Michael V.; Ludwig, William D.

2010-04-01

A research-oriented Army Technology Objective (ATO) named Sensor and Information Fusion for Improved Hostile Fire Situational Awareness uniquely focuses on the underpinning technologies to detect and defeat any hostile threat; before, during, and after its occurrence. This is a joint effort led by the Army Research Laboratory, with the Armaments and the Communications and Electronics Research, Development, and Engineering Centers (CERDEC and ARDEC) partners. It addresses distributed sensor fusion and collaborative situational awareness enhancements, focusing on the underpinning technologies to detect/identify potential hostile shooters prior to firing a shot and to detect/classify/locate the firing point of hostile small arms, mortars, rockets, RPGs, and missiles after the first shot. A field experiment conducted addressed not only diverse modality sensor performance and sensor fusion benefits, but gathered useful data to develop and demonstrate the ad hoc networking and dissemination of relevant data and actionable intelligence. Represented at this field experiment were various sensor platforms such as UGS, soldier-worn, manned ground vehicles, UGVs, UAVs, and helicopters. This ATO continues to evaluate applicable technologies to include retro-reflection, UV, IR, visible, glint, LADAR, radar, acoustic, seismic, E-field, narrow-band emission and image processing techniques to detect the threats with very high confidence. Networked fusion of multi-modal data will reduce false alarms and improve actionable intelligence by distributing grid coordinates, detection report features, and imagery of threats.

Effect of retransmission and retrodiction on estimation and fusion in long-haul sensor networks

DOE PAGES

Liu, Qiang; Wang, Xin; Rao, Nageswara S. V.; ...

2016-01-01

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this work, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can alsomore » degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Besides, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Lastly, simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.« less

Minimum energy information fusion in sensor networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chapline, G

1999-05-11

In this paper we consider how to organize the sharing of information in a distributed network of sensors and data processors so as to provide explanations for sensor readings with minimal expenditure of energy. We point out that the Minimum Description Length principle provides an approach to information fusion that is more naturally suited to energy minimization than traditional Bayesian approaches. In addition we show that for networks consisting of a large number of identical sensors Kohonen self-organization provides an exact solution to the problem of combing the sensor outputs into minimal description length explanations.

Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks.

PubMed

Bergamini, Elena; Ligorio, Gabriele; Summa, Aurora; Vannozzi, Giuseppe; Cappozzo, Aurelio; Sabatini, Angelo Maria

2014-10-09

Magnetic and inertial measurement units are an emerging technology to obtain 3D orientation of body segments in human movement analysis. In this respect, sensor fusion is used to limit the drift errors resulting from the gyroscope data integration by exploiting accelerometer and magnetic aiding sensors. The present study aims at investigating the effectiveness of sensor fusion methods under different experimental conditions. Manual and locomotion tasks, differing in time duration, measurement volume, presence/absence of static phases, and out-of-plane movements, were performed by six subjects, and recorded by one unit located on the forearm or the lower trunk, respectively. Two sensor fusion methods, representative of the stochastic (Extended Kalman Filter) and complementary (Non-linear observer) filtering, were selected, and their accuracy was assessed in terms of attitude (pitch and roll angles) and heading (yaw angle) errors using stereophotogrammetric data as a reference. The sensor fusion approaches provided significantly more accurate results than gyroscope data integration. Accuracy improved mostly for heading and when the movement exhibited stationary phases, evenly distributed 3D rotations, it occurred in a small volume, and its duration was greater than approximately 20 s. These results were independent from the specific sensor fusion method used. Practice guidelines for improving the outcome accuracy are provided.

SVM-based multi-sensor fusion for free-living physical activity assessment.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty S

2011-01-01

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on the support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multi-sensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which the activity types and related energy expenditures are derived. The result shows that the method correctly recognized the 13 activity types 84.7% of the time, which is 26% higher than using a hip accelerometer alone. Also, the method predicted the associated energy expenditure with a root mean square error of 0.43 METs, 43% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor was added to the fusion model. These results demonstrate that the multi-sensor fusion technique presented is more effective in assessing activities of varying intensities than the traditional accelerometer-alone based methods.

Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter

PubMed Central

Chowdhury, Amor; Sarjaš, Andrej

2016-01-01

The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation. PMID:27649197

Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter.

PubMed

Chowdhury, Amor; Sarjaš, Andrej

2016-09-15

The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

Fusion of intraoperative force sensoring, surface reconstruction and biomechanical modeling

NASA Astrophysics Data System (ADS)

Röhl, S.; Bodenstedt, S.; Küderle, C.; Suwelack, S.; Kenngott, H.; Müller-Stich, B. P.; Dillmann, R.; Speidel, S.

2012-02-01

Minimally invasive surgery is medically complex and can heavily benefit from computer assistance. One way to help the surgeon is to integrate preoperative planning data into the surgical workflow. This information can be represented as a customized preoperative model of the surgical site. To use it intraoperatively, it has to be updated during the intervention due to the constantly changing environment. Hence, intraoperative sensor data has to be acquired and registered with the preoperative model. Haptic information which could complement the visual sensor data is still not established. In addition, biomechanical modeling of the surgical site can help in reflecting the changes which cannot be captured by intraoperative sensors. We present a setting where a force sensor is integrated into a laparoscopic instrument. In a test scenario using a silicone liver phantom, we register the measured forces with a reconstructed surface model from stereo endoscopic images and a finite element model. The endoscope, the instrument and the liver phantom are tracked with a Polaris optical tracking system. By fusing this information, we can transfer the deformation onto the finite element model. The purpose of this setting is to demonstrate the principles needed and the methods developed for intraoperative sensor data fusion. One emphasis lies on the calibration of the force sensor with the instrument and first experiments with soft tissue. We also present our solution and first results concerning the integration of the force sensor as well as accuracy to the fusion of force measurements, surface reconstruction and biomechanical modeling.

MMWR/FLIR/ATR sensor fusion: Proof of concept

NASA Astrophysics Data System (ADS)

Woolett, Jerry F.

1988-06-01

To improve the relocatable target capabilities of strategic aircraft a sensor fusion concept using a millimeter-wave radar (MMWR) and a forward-looking infrared (FLIR) system providing inputs to an auto target recognizer (ATR) has been developed. To prove this concept, a cooperative research effort is being conducted by a group of industry leaders in bomber avionics, MMWR, and ATR technologies. The author discusses the concept and the plan developed to test, evaluate, and demonstrate the expected performance.

The life and death of ATR/sensor fusion and the hope for resurrection

NASA Astrophysics Data System (ADS)

Rogers, Steven K.; Sadowski, Charles; Bauer, Kenneth W.; Oxley, Mark E.; Kabrisky, Matthew; Rogers, Adam; Mott, Stephen D.

2008-04-01

For over half a century, scientists and engineers have worked diligently to advance computational intelligence. One application of interest is how computational intelligence can bring value to our war fighters. Automatic Target Recognition (ATR) and sensor fusion efforts have fallen far short of the desired capabilities. In this article we review the capabilities requested by war fighters. When compared to our current capabilities, it is easy to conclude current Combat Identification (CID) as a Family of Systems (FoS) does a lousy job. The war fighter needed capable, operationalized ATR and sensor fusion systems ten years ago but it did not happen. The article reviews the war fighter needs and the current state of the art. The article then concludes by looking forward to where we are headed to provide the capabilities required.

Application of Sensor Fusion to Improve Uav Image Classification

NASA Astrophysics Data System (ADS)

Jabari, S.; Fathollahi, F.; Zhang, Y.

2017-08-01

Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

A Locomotion Intent Prediction System Based on Multi-Sensor Fusion

PubMed Central

Chen, Baojun; Zheng, Enhao; Wang, Qining

2014-01-01

Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers. PMID:25014097

A locomotion intent prediction system based on multi-sensor fusion.

PubMed

Chen, Baojun; Zheng, Enhao; Wang, Qining

2014-07-10

Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.

Knowledge-based imaging-sensor fusion system

NASA Technical Reports Server (NTRS)

Westrom, George

1989-01-01

An imaging system which applies knowledge-based technology to supervise and control both sensor hardware and computation in the imaging system is described. It includes the development of an imaging system breadboard which brings together into one system work that we and others have pursued for LaRC for several years. The goal is to combine Digital Signal Processing (DSP) with Knowledge-Based Processing and also include Neural Net processing. The system is considered a smart camera. Imagine that there is a microgravity experiment on-board Space Station Freedom with a high frame rate, high resolution camera. All the data cannot possibly be acquired from a laboratory on Earth. In fact, only a small fraction of the data will be received. Again, imagine being responsible for some experiments on Mars with the Mars Rover: the data rate is a few kilobits per second for data from several sensors and instruments. Would it not be preferable to have a smart system which would have some human knowledge and yet follow some instructions and attempt to make the best use of the limited bandwidth for transmission. The system concept, current status of the breadboard system and some recent experiments at the Mars-like Amboy Lava Fields in California are discussed.

Model-theoretic framework for sensor data fusion

NASA Astrophysics Data System (ADS)

Zavoleas, Kyriakos P.; Kokar, Mieczyslaw M.

1993-09-01

The main goal of our research in sensory data fusion (SDF) is the development of a systematic approach (a methodology) to designing systems for interpreting sensory information and for reasoning about the situation based upon this information and upon available data bases and knowledge bases. To achieve such a goal, two kinds of subgoals have been set: (1) develop a theoretical framework in which rational design/implementation decisions can be made, and (2) design a prototype SDF system along the lines of the framework. Our initial design of the framework has been described in our previous papers. In this paper we concentrate on the model-theoretic aspects of this framework. We postulate that data are embedded in data models, and information processing mechanisms are embedded in model operators. The paper is devoted to analyzing the classes of model operators and their significance in SDF. We investigate transformation abstraction and fusion operators. A prototype SDF system, fusing data from range and intensity sensors, is presented, exemplifying the structures introduced. Our framework is justified by the fact that it provides modularity, traceability of information flow, and a basis for a specification language for SDF.

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements.

PubMed

Tannous, Halim; Istrate, Dan; Benlarbi-Delai, Aziz; Sarrazin, Julien; Gamet, Didier; Ho Ba Tho, Marie Christine; Dao, Tien Tuan

2016-11-15

Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject's movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

Data Fusion for a Vision-Radiological System: a Statistical Calibration Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Enqvist, Andreas; Koppal, Sanjeev; Riley, Phillip

2015-07-01

Presented here is a fusion system based on simple, low-cost computer vision and radiological sensors for tracking of multiple objects and identifying potential radiological materials being transported or shipped. The main focus of this work is the development of calibration algorithms for characterizing the fused sensor system as a single entity. There is an apparent need for correcting for a scene deviation from the basic inverse distance-squared law governing the detection rates even when evaluating system calibration algorithms. In particular, the computer vision system enables a map of distance-dependence of the sources being tracked, to which the time-dependent radiological datamore » can be incorporated by means of data fusion of the two sensors' output data. (authors)« less

Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

NASA Astrophysics Data System (ADS)

Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.

2011-02-01

Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.

Staggered scheduling of sensor estimation and fusion for tracking over long-haul links

DOE PAGES

Liu, Qiang; Rao, Nageswara S. V.; Wang, Xin

2016-08-01

Networked sensing can be found in a multitude of real-world applications. Here, we focus on the communication-and computation-constrained long-haul sensor networks, where sensors are remotely deployed over a vast geographical area to perform certain tasks. Of special interest is a class of such networks where sensors take measurements of one or more dynamic targets and send their state estimates to a remote fusion center via long-haul satellite links. The severe loss and delay over such links can easily reduce the amount of sensor data received by the fusion center, thereby limiting the potential information fusion gain and resulting in suboptimalmore » tracking performance. In this paper, starting with the temporal-domain staggered estimation for an individual sensor, we explore the impact of the so-called intra-state prediction and retrodiction on estimation errors. We then investigate the effect of such estimation scheduling across different sensors on the spatial-domain fusion performance, where the sensing time epochs across sensors are scheduled in an asynchronous and staggered manner. In particular, the impact of communication delay and loss as well as sensor bias on such scheduling is explored by means of numerical and simulation studies that demonstrate the validity of our analysis.« less

Fusion or confusion: knowledge or nonsense?

NASA Astrophysics Data System (ADS)

Rothman, Peter L.; Denton, Richard V.

1991-08-01

The terms 'data fusion,' 'sensor fusion,' multi-sensor integration,' and 'multi-source integration' have been used widely in the technical literature to refer to a variety of techniques, technologies, systems, and applications which employ and/or combine data derived from multiple information sources. Applications of data fusion range from real-time fusion of sensor information for the navigation of mobile robots to the off-line fusion of both human and technical strategic intelligence data. The Department of Defense Critical Technologies Plan lists data fusion in the highest priority group of critical technologies, but just what is data fusion? The DoD Critical Technologies Plan states that data fusion involves 'the acquisition, integration, filtering, correlation, and synthesis of useful data from diverse sources for the purposes of situation/environment assessment, planning, detecting, verifying, diagnosing problems, aiding tactical and strategic decisions, and improving system performance and utility.' More simply states, sensor fusion refers to the combination of data from multiple sources to provide enhanced information quality and availability over that which is available from any individual source alone. This paper presents a survey of the state-of-the- art in data fusion technologies, system components, and applications. A set of characteristics which can be utilized to classify data fusion systems is presented. Additionally, a unifying mathematical and conceptual framework within which to understand and organize fusion technologies is described. A discussion of often overlooked issues in the development of sensor fusion systems is also presented.

Wearable sensor systems for infants.

PubMed

Zhu, Zhihua; Liu, Tao; Li, Guangyi; Li, Tong; Inoue, Yoshio

2015-02-05

Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant's body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future.

Wearable Sensor Systems for Infants

PubMed Central

Zhu, Zhihua; Liu, Tao; Li, Guangyi; Li, Tong; Inoue, Yoshio

2015-01-01

Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant's body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future. PMID:25664432

Data fusion for target tracking and classification with wireless sensor network

NASA Astrophysics Data System (ADS)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

PubMed

Jiang, Baichuan; Gao, Wenpeng; Kacher, Daniel; Nevo, Erez; Fetics, Barry; Lee, Thomas C; Jayender, Jagadeesan

2018-04-01

In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.

Noncontact Sleep Study by Multi-Modal Sensor Fusion.

PubMed

Chung, Ku-Young; Song, Kwangsub; Shin, Kangsoo; Sohn, Jinho; Cho, Seok Hyun; Chang, Joon-Hyuk

2017-07-21

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

Noncontact Sleep Study by Multi-Modal Sensor Fusion

PubMed Central

Chung, Ku-young; Song, Kwangsub; Shin, Kangsoo; Sohn, Jinho; Cho, Seok Hyun; Chang, Joon-Hyuk

2017-01-01

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner. PMID:28753994

A robust vision-based sensor fusion approach for real-time pose estimation.

PubMed

Assa, Akbar; Janabi-Sharifi, Farrokh

2014-02-01

Object pose estimation is of great importance to many applications, such as augmented reality, localization and mapping, motion capture, and visual servoing. Although many approaches based on a monocular camera have been proposed, only a few works have concentrated on applying multicamera sensor fusion techniques to pose estimation. Higher accuracy and enhanced robustness toward sensor defects or failures are some of the advantages of these schemes. This paper presents a new Kalman-based sensor fusion approach for pose estimation that offers higher accuracy and precision, and is robust to camera motion and image occlusion, compared to its predecessors. Extensive experiments are conducted to validate the superiority of this fusion method over currently employed vision-based pose estimation algorithms.

Sensor Fusion Techniques for Phased-Array Eddy Current and Phased-Array Ultrasound Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arrowood, Lloyd F.

Sensor (or Data) fusion is the process of integrating multiple data sources to produce more consistent, accurate and comprehensive information than is provided by a single data source. Sensor fusion may also be used to combine multiple signals from a single modality to improve the performance of a particular inspection technique. Industrial nondestructive testing may utilize multiple sensors to acquire inspection data depending upon the object under inspection and the anticipated types of defects that can be identified. Sensor fusion can be performed at various levels of signal abstraction with each having its strengths and weaknesses. A multimodal data fusionmore » strategy first proposed by Heideklang and Shokouhi that combines spatially scattered detection locations to improve detection performance of surface-breaking and near-surface cracks in ferromagnetic metals is shown using a surface inspection example and is then extended for volumetric inspections. Utilizing data acquired from an Olympus Omniscan MX2 from both phased array eddy current and ultrasound probes on test phantoms, single and multilevel fusion techniques are employed to integrate signals from the two modalities. Preliminary results demonstrate how confidence in defect identification and interpretation benefit from sensor fusion techniques. Lastly, techniques for integrating data into radiographic and volumetric imagery from computed tomography are described and results are presented.« less

Embry-Riddle Aeronautical University multispectral sensor and data fusion laboratory: a model for distributed research and education

NASA Astrophysics Data System (ADS)

McMullen, Sonya A. H.; Henderson, Troy; Ison, David

2017-05-01

The miniaturization of unmanned systems and spacecraft, as well as computing and sensor technologies, has opened new opportunities in the areas of remote sensing and multi-sensor data fusion for a variety of applications. Remote sensing and data fusion historically have been the purview of large government organizations, such as the Department of Defense (DoD), National Aeronautics and Space Administration (NASA), and National Geospatial-Intelligence Agency (NGA) due to the high cost and complexity of developing, fielding, and operating such systems. However, miniaturized computers with high capacity processing capabilities, small and affordable sensors, and emerging, commercially available platforms such as UAS and CubeSats to carry such sensors, have allowed for a vast range of novel applications. In order to leverage these developments, Embry-Riddle Aeronautical University (ERAU) has developed an advanced sensor and data fusion laboratory to research component capabilities and their employment on a wide-range of autonomous, robotic, and transportation systems. This lab is unique in several ways, for example, it provides a traditional campus laboratory for students and faculty to model and test sensors in a range of scenarios, process multi-sensor data sets (both simulated and experimental), and analyze results. Moreover, such allows for "virtual" modeling, testing, and teaching capability reaching beyond the physical confines of the facility for use among ERAU Worldwide students and faculty located around the globe. Although other institutions such as Georgia Institute of Technology, Lockheed Martin, University of Dayton, and University of Central Florida have optical sensor laboratories, the ERAU virtual concept is the first such lab to expand to multispectral sensors and data fusion, while focusing on the data collection and data products and not on the manufacturing aspect. Further, the initiative is a unique effort among Embry-Riddle faculty to develop multi

Sensor data fusion for textured reconstruction and virtual representation of alpine scenes

NASA Astrophysics Data System (ADS)

Häufel, Gisela; Bulatov, Dimitri; Solbrig, Peter

2017-10-01

The concept of remote sensing is to provide information about a wide-range area without making physical contact with this area. If, additionally to satellite imagery, images and videos taken by drones provide a more up-to-date data at a higher resolution, or accurate vector data is downloadable from the Internet, one speaks of sensor data fusion. The concept of sensor data fusion is relevant for many applications, such as virtual tourism, automatic navigation, hazard assessment, etc. In this work, we describe sensor data fusion aiming to create a semantic 3D model of an extremely interesting yet challenging dataset: An alpine region in Southern Germany. A particular challenge of this work is that rock faces including overhangs are present in the input airborne laser point cloud. The proposed procedure for identification and reconstruction of overhangs from point clouds comprises four steps: Point cloud preparation, filtering out vegetation, mesh generation and texturing. Further object types are extracted in several interesting subsections of the dataset: Building models with textures from UAV (Unmanned Aerial Vehicle) videos, hills reconstructed as generic surfaces and textured by the orthophoto, individual trees detected by the watershed algorithm, as well as the vector data for roads retrieved from openly available shapefiles and GPS-device tracks. We pursue geo-specific reconstruction by assigning texture and width to roads of several pre-determined types and modeling isolated trees and rocks using commercial software. For visualization and simulation of the area, we have chosen the simulation system Virtual Battlespace 3 (VBS3). It becomes clear that the proposed concept of sensor data fusion allows a coarse reconstruction of a large scene and, at the same time, an accurate and up-to-date representation of its relevant subsections, in which simulation can take place.

Information Fusion in Ad hoc Wireless Sensor Networks for Aircraft Health Monitoring

NASA Astrophysics Data System (ADS)

Fragoulis, Nikos; Tsagaris, Vassilis; Anastassopoulos, Vassilis

In this paper the use of an ad hoc wireless sensor network for implementing a structural health monitoring system is discussed. The network is consisted of sensors deployed throughout the aircraft. These sensors being in the form of a microelectronic chip and consisted of sensing, data processing and communicating components could be easily embedded in any mechanical aircraft component. The established sensor network, due to its ad hoc nature is easily scalable, allowing adding or removing any number of sensors. The position of the sensor nodes need not necessarily to be engineered or predetermined, giving this way the ability to be deployed in inaccessible points. Information collected from various sensors of different modalities throughout the aircraft is then fused in order to provide a more comprehensive image of the aircraft structural health. Sensor level fusion along with decision quality information is used, in order to enhance detection performance.

The addition of a sagittal image fusion improves the prostate cancer detection in a sensor-based MRI /ultrasound fusion guided targeted biopsy.

PubMed

Günzel, Karsten; Cash, Hannes; Buckendahl, John; Königbauer, Maximilian; Asbach, Patrick; Haas, Matthias; Neymeyer, Jörg; Hinz, Stefan; Miller, Kurt; Kempkensteffen, Carsten

2017-01-13

To explore the diagnostic benefit of an additional image fusion of the sagittal plane in addition to the standard axial image fusion, using a sensor-based MRI/US fusion platform. During July 2013 and September 2015, 251 patients with at least one suspicious lesion on mpMRI (rated by PI-RADS) were included into the analysis. All patients underwent MRI/US targeted biopsy (TB) in combination with a 10 core systematic prostate biopsy (SB). All biopsies were performed on a sensor-based fusion system. Group A included 162 men who received TB by an axial MRI/US image fusion. Group B comprised 89 men in whom the TB was performed with an additional sagittal image fusion. The median age in group A was 67 years (IQR 61-72) and in group B 68 years (IQR 60-71). The median PSA level in group A was 8.10 ng/ml (IQR 6.05-14) and in group B 8.59 ng/ml (IQR 5.65-12.32). In group A the proportion of patients with a suspicious digital rectal examination (DRE) (14 vs. 29%, p = 0.007) and the proportion of primary biopsies (33 vs 46%, p = 0.046) were significantly lower. The rate of PI-RADS 3 lesions were overrepresented in group A compared to group B (19 vs. 9%; p = 0.044). Classified according to PI-RADS 3, 4 and 5, the detection rates of TB were 42, 48, 75% in group A and 25, 74, 90% in group B. The rate of PCa with a Gleason score ≥7 missed by TB was 33% (18 cases) in group A and 9% (5 cases) in group B; p-value 0.072. An explorative multivariate binary logistic regression analysis revealed that PI-RADS, a suspicious DRE and performing an additional sagittal image fusion were significant predictors for PCa detection in TB. 9 PCa were only detected by TB with sagittal fusion (sTB) and sTB identified 10 additional clinically significant PCa (Gleason ≥7). Performing an additional sagittal image fusion besides the standard axial fusion appears to improve the accuracy of the sensor-based MRI/US fusion platform.

IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.

PubMed

Dehzangi, Omid; Taherisadr, Mojtaba; ChangalVala, Raghvendar

2017-11-27

. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively.

A trainable decisions-in decision-out (DEI-DEO) fusion system

NASA Astrophysics Data System (ADS)

Dasarathy, Belur V.

1998-03-01

Most of the decision fusion systems proposed hitherto in the literature for multiple data source (sensor) environments operate on the basis of pre-defined fusion logic, be they crisp (deterministic), probabilistic, or fuzzy in nature, with no specific learning phase. The fusion systems that are trainable, i.e., ones that have a learning phase, mostly operate in the features-in-decision-out mode, which essentially reduces the fusion process functionally to a pattern classification task in the joint feature space. In this study, a trainable decisions-in-decision-out fusion system is described which estimates a fuzzy membership distribution spread across the different decision choices based on the performance of the different decision processors (sensors) corresponding to each training sample (object) which is associated with a specific ground truth (true decision). Based on a multi-decision space histogram analysis of the performance of the different processors over the entire training data set, a look-up table associating each cell of the histogram with a specific true decision is generated which forms the basis for the operational phase. In the operational phase, for each set of decision inputs, a pointer to the look-up table learnt previously is generated from which a fused decision is derived. This methodology, although primarily designed for fusing crisp decisions from the multiple decision sources, can be adapted for fusion of fuzzy decisions as well if such are the inputs from these sources. Examples, which illustrate the benefits and limitations of the crisp and fuzzy versions of the trainable fusion systems, are also included.

Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

PubMed Central

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

PubMed

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

Embedded Relative Navigation Sensor Fusion Algorithms for Autonomous Rendezvous and Docking Missions

NASA Technical Reports Server (NTRS)

DeKock, Brandon K.; Betts, Kevin M.; McDuffie, James H.; Dreas, Christine B.

2008-01-01

bd Systems (a subsidiary of SAIC) has developed a suite of embedded relative navigation sensor fusion algorithms to enable NASA autonomous rendezvous and docking (AR&D) missions. Translational and rotational Extended Kalman Filters (EKFs) were developed for integrating measurements based on the vehicles' orbital mechanics and high-fidelity sensor error models and provide a solution with increased accuracy and robustness relative to any single relative navigation sensor. The filters were tested tinough stand-alone covariance analysis, closed-loop testing with a high-fidelity multi-body orbital simulation, and hardware-in-the-loop (HWIL) testing in the Marshall Space Flight Center (MSFC) Flight Robotics Laboratory (FRL).

Enhanced technologies for unattended ground sensor systems

NASA Astrophysics Data System (ADS)

Hartup, David C.

2010-04-01

Progress in several technical areas is being leveraged to advantage in Unattended Ground Sensor (UGS) systems. This paper discusses advanced technologies that are appropriate for use in UGS systems. While some technologies provide evolutionary improvements, other technologies result in revolutionary performance advancements for UGS systems. Some specific technologies discussed include wireless cameras and viewers, commercial PDA-based system programmers and monitors, new materials and techniques for packaging improvements, low power cueing sensor radios, advanced long-haul terrestrial and SATCOM radios, and networked communications. Other technologies covered include advanced target detection algorithms, high pixel count cameras for license plate and facial recognition, small cameras that provide large stand-off distances, video transmissions of target activity instead of still images, sensor fusion algorithms, and control center hardware. The impact of each technology on the overall UGS system architecture is discussed, along with the advantages provided to UGS system users. Areas of analysis include required camera parameters as a function of stand-off distance for license plate and facial recognition applications, power consumption for wireless cameras and viewers, sensor fusion communication requirements, and requirements to practically implement video transmission through UGS systems. Examples of devices that have already been fielded using technology from several of these areas are given.

On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection.

PubMed

Tsinganos, Panagiotis; Skodras, Athanassios

2018-02-14

In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical attention; thus, their detection is of primary importance. To this effect, many fall detection systems that utilize wearable and ambient sensors have been proposed. In this study, we compare three newly proposed data fusion schemes that have been applied in human activity recognition and fall detection. Furthermore, these algorithms are compared to our recent work regarding fall detection in which only one type of sensor is used. The results show that fusion algorithms differ in their performance, whereas a machine learning strategy should be preferred. In conclusion, the methods presented and the comparison of their performance provide useful insights into the problem of fall detection.

A novel framework for command and control of networked sensor systems

NASA Astrophysics Data System (ADS)

Chen, Genshe; Tian, Zhi; Shen, Dan; Blasch, Erik; Pham, Khanh

2007-04-01

In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and mitigation for future military operations by graphical game theory and advanced knowledge information fusion infrastructures. The problem is approached by representing distributed sensor and weapon systems as generic warfare resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems. This paper addresses the importance of achieving a Network Centric Warfare (NCW) foundation of information superiority-shared, accurate, and timely situational awareness upon which advanced automated management aids for IFC can be built. The approach uses the Data Fusion Information Group (DFIG) Fusion hierarchy of Level 0 through Level 4 to fuse the input data into assessments for the enemy target system threats in a battlespace to which military force is being applied. Compact graph models are employed across all levels of the fusion hierarchy to accomplish integrative data fusion and information flow control, as well as cross-layer sensor management. The functional block at each fusion level will have a set of innovative algorithms that not only exploit the corresponding graph model in a computationally efficient manner, but also permit combined functional experiments across levels by virtue of the unifying graphical model approach.

A Fusion Architecture for Tracking a Group of People Using a Distributed Sensor Network

DTIC Science & Technology

2013-07-01

Determining the composition of the group is done using several classifiers. The fusion is done at the UGS level to fuse information from all the modalities to...to classification and counting of the targets. Section III also presents the algorithms for fusion of distributed sensor data at the UGS level and...ultrasonic sensors. Determining the composition of the group is done using several classifiers. The fusion is done at the UGS level to fuse

A practical approach for active camera coordination based on a fusion-driven multi-agent system

NASA Astrophysics Data System (ADS)

Bustamante, Alvaro Luis; Molina, José M.; Patricio, Miguel A.

2014-04-01

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.

Data Fusion Analysis for Range Test Validation System

DTIC Science & Technology

2010-07-14

simulants were released during the RTVS ’08 test series: triethyl phosphate (TEP), methyl salicylate (MeS), and acetic acid (AA). A total of 29 release...the combination of a grid of point sensors at ground level and a standoff FTIR system monitoring above ground areas proved effective in detecting the...presence of simulants over the test grid. A Dempster-Shafer approach for data fusion was selected as the most effective strategy for RTVS data fusion

Data fusion algorithm for rapid multi-mode dust concentration measurement system based on MEMS

NASA Astrophysics Data System (ADS)

Liao, Maohao; Lou, Wenzhong; Wang, Jinkui; Zhang, Yan

2018-03-01

As single measurement method cannot fully meet the technical requirements of dust concentration measurement, the multi-mode detection method is put forward, as well as the new requirements for data processing. This paper presents a new dust concentration measurement system which contains MEMS ultrasonic sensor and MEMS capacitance sensor, and presents a new data fusion algorithm for this multi-mode dust concentration measurement system. After analyzing the relation between the data of the composite measurement method, the data fusion algorithm based on Kalman filtering is established, which effectively improve the measurement accuracy, and ultimately forms a rapid data fusion model of dust concentration measurement. Test results show that the data fusion algorithm is able to realize the rapid and exact concentration detection.

The Human Factors of Sensor Fusion

DTIC Science & Technology

2008-05-01

tool in future military operations. 23 7. References Abdi, H. Neural Networks. In M. Lewis-Beck, A . Bryman , T. Futing (Eds), Encyclopedia for... a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1...This report discusses select, cognitively based principles associated with the sensor fusion process. A review is made of the standard

Sensor data fusion for spectroscopy-based detection of explosives

NASA Astrophysics Data System (ADS)

Shah, Pratik V.; Singh, Abhijeet; Agarwal, Sanjeev; Sedigh, Sahra; Ford, Alan; Waterbury, Robert

2009-05-01

In-situ trace detection of explosive compounds such as RDX, TNT, and ammonium nitrate, is an important problem for the detection of IEDs and IED precursors. Spectroscopic techniques such as LIBS and Raman have shown promise for the detection of residues of explosive compounds on surfaces from standoff distances. Individually, both LIBS and Raman techniques suffer from various limitations, e.g., their robustness and reliability suffers due to variations in peak strengths and locations. However, the orthogonal nature of the spectral and compositional information provided by these techniques makes them suitable candidates for the use of sensor fusion to improve the overall detection performance. In this paper, we utilize peak energies in a region by fitting Lorentzian or Gaussian peaks around the location of interest. The ratios of peak energies are used for discrimination, in order to normalize the effect of changes in overall signal strength. Two data fusion techniques are discussed in this paper. Multi-spot fusion is performed on a set of independent samples from the same region based on the maximum likelihood formulation. Furthermore, the results from LIBS and Raman sensors are fused using linear discriminators. Improved detection performance with significantly reduced false alarm rates is reported using fusion techniques on data collected for sponsor demonstration at Fort Leonard Wood.

Fusion solution for soldier wearable gunfire detection systems

NASA Astrophysics Data System (ADS)

Cakiades, George; Desai, Sachi; Deligeorges, Socrates; Buckland, Bruce E.; George, Jemin

2012-06-01

Currently existing acoustic based Gunfire Detection Systems (GDS) such as soldier wearable, vehicle mounted, and fixed site devices provide enemy detection and localization capabilities to the user. However, the solution to the problem of portability versus performance tradeoff remains elusive. The Data Fusion Module (DFM), described herein, is a sensor/platform agnostic software supplemental tool that addresses this tradeoff problem by leveraging existing soldier networks to enhance GDS performance across a Tactical Combat Unit (TCU). The DFM software enhances performance by leveraging all available acoustic GDS information across the TCU synergistically to calculate highly accurate solutions more consistently than any individual GDS in the TCU. The networked sensor architecture provides additional capabilities addressing the multiple shooter/fire-fight problems in addition to sniper detection/localization. The addition of the fusion solution to the overall Size, Weight and Power & Cost (SWaP&C) is zero to negligible. At the end of the first-year effort, the DFM integrated sensor network's performance was impressive showing improvements upwards of 50% in comparison to a single sensor solution. Further improvements are expected when the networked sensor architecture created in this effort is fully exploited.

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

PubMed Central

Mokhlespour Esfahani, Mohammad Iman; Zobeiri, Omid; Moshiri, Behzad; Narimani, Roya; Mehravar, Mohammad; Rashedi, Ehsan; Parnianpour, Mohamad

2017-01-01

Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions. PMID:28075342

Sensor data fusion of radar, ESM, IFF, and data LINK of the Canadian Patrol Frigate and the data alignment issues

NASA Astrophysics Data System (ADS)

Couture, Jean; Boily, Edouard; Simard, Marc-Alain

1996-05-01

The research and development group at Loral Canada is now at the second phase of the development of a data fusion demonstration model (DFDM) for a naval anti-air warfare to be used as a workbench tool to perform exploratory research. This project has emphatically addressed how the concepts related to fusion could be implemented within the Canadian Patrol Frigate (CPF) software environment. The project has been designed to read data passively on the CPF bus without any modification to the CPF software. This has brought to light important time alignment issues since the CPF sensors and the CPF command and control system were not important time alignment issues since the CPF sensors and the CPF command and control system were not originally designed to support a track management function which fuses information. The fusion of data from non-organic sensors with the tactical Link-11 data has produced stimulating spatial alignment problems which have been overcome by the use of a geodetic referencing coordinate system. Some benchmark scenarios have been selected to quantitatively demonstrate the capabilities of this fusion implementation. This paper describes the implementation design of DFDM (version 2), and summarizes the results obtained so far when fusing the scenarios simulated data.

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach

PubMed Central

Girrbach, Fabian; Hol, Jeroen D.; Bellusci, Giovanni; Diehl, Moritz

2017-01-01

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. PMID:28534857

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach.

PubMed

Girrbach, Fabian; Hol, Jeroen D; Bellusci, Giovanni; Diehl, Moritz

2017-05-19

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.

Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion.

PubMed

Zhu, Yongwei; Fook, Victor Foo Siang; Jianzhong, Emily Hao; Maniyeri, Jayachandran; Guan, Cuntai; Zhang, Haihong; Jiliang, Eugene Phua; Biswas, Jit

2014-01-01

This paper presents a method of estimating heart rate from arrays of fiber Bragg grating (FBG) sensors embedded in a mat. A cepstral domain signal analysis technique is proposed to characterize Ballistocardiogram (BCG) signals. With this technique, the average heart beat intervals can be estimated by detecting the dominant peaks in the cepstrum, and the signals of multiple sensors can be fused together to obtain higher signal to noise ratio than each individual sensor. Experiments were conducted with 10 human subjects lying on 2 different postures on a bed. The estimated heart rate from BCG was compared with heart rate ground truth from ECG, and the mean error of estimation obtained is below 1 beat per minute (BPM). The results show that the proposed fusion method can achieve promising heart rate measurement accuracy and robustness against various sensor contact conditions.

Exception handling for sensor fusion

NASA Astrophysics Data System (ADS)

Chavez, G. T.; Murphy, Robin R.

1993-08-01

This paper presents a control scheme for handling sensing failures (sensor malfunctions, significant degradations in performance due to changes in the environment, and errant expectations) in sensor fusion for autonomous mobile robots. The advantages of the exception handling mechanism are that it emphasizes a fast response to sensing failures, is able to use only a partial causal model of sensing failure, and leads to a graceful degradation of sensing if the sensing failure cannot be compensated for. The exception handling mechanism consists of two modules: error classification and error recovery. The error classification module in the exception handler attempts to classify the type and source(s) of the error using a modified generate-and-test procedure. If the source of the error is isolated, the error recovery module examines its cache of recovery schemes, which either repair or replace the current sensing configuration. If the failure is due to an error in expectation or cannot be identified, the planner is alerted. Experiments using actual sensor data collected by the CSM Mobile Robotics/Machine Perception Laboratory's Denning mobile robot demonstrate the operation of the exception handling mechanism.

Fault tolerant multi-sensor fusion based on the information gain

NASA Astrophysics Data System (ADS)

Hage, Joelle Al; El Najjar, Maan E.; Pomorski, Denis

2017-01-01

In the last decade, multi-robot systems are used in several applications like for example, the army, the intervention areas presenting danger to human life, the management of natural disasters, the environmental monitoring, exploration and agriculture. The integrity of localization of the robots must be ensured in order to achieve their mission in the best conditions. Robots are equipped with proprioceptive (encoders, gyroscope) and exteroceptive sensors (Kinect). However, these sensors could be affected by various faults types that can be assimilated to erroneous measurements, bias, outliers, drifts,… In absence of a sensor fault diagnosis step, the integrity and the continuity of the localization are affected. In this work, we present a muti-sensors fusion approach with Fault Detection and Exclusion (FDE) based on the information theory. In this context, we are interested by the information gain given by an observation which may be relevant when dealing with the fault tolerance aspect. Moreover, threshold optimization based on the quantity of information given by a decision on the true hypothesis is highlighted.

Image fusion

NASA Technical Reports Server (NTRS)

Pavel, M.

1993-01-01

The topics covered include the following: a system overview of the basic components of a system designed to improve the ability of a pilot to fly through low-visibility conditions such as fog; the role of visual sciences; fusion issues; sensor characterization; sources of information; image processing; and image fusion.

Summary of sensor evaluation for the Fusion Electromagnetic Induction Experiment (FELIX)

NASA Astrophysics Data System (ADS)

Knott, M. J.

1982-08-01

As part of the First Wall/Blanket/Shield Engineering Test Program, a test bed called FELIX (fusion electromagnetic induction experiment) is under construction. Its purpose is to test, evaluate, and develop computer codes for the prediction of electromagnetically induced phenomenon in a magnetic environment modeling that of a fusion reaction. Crucial to this process is the sensing and recording of the various induced effects. Sensor evaluation for FELIX reached the point where most sensor types were evaluated and preliminary decisions are being made as to type and quantity for the initial FELIX experiments. These early experiments, the first, flat plate experiment in particular, will be aimed at testing the sensors as well as the pertinent theories involved. The reason for these evaluations, decisions, and proof tests is the harsh electrical and magnetic environment that FELIX presents.

Interactive Scene Analysis Module - A sensor-database fusion system for telerobotic environments

NASA Technical Reports Server (NTRS)

Cooper, Eric G.; Vazquez, Sixto L.; Goode, Plesent W.

1992-01-01

Accomplishing a task with telerobotics typically involves a combination of operator control/supervision and a 'script' of preprogrammed commands. These commands usually assume that the location of various objects in the task space conform to some internal representation (database) of that task space. The ability to quickly and accurately verify the task environment against the internal database would improve the robustness of these preprogrammed commands. In addition, the on-line initialization and maintenance of a task space database is difficult for operators using Cartesian coordinates alone. This paper describes the Interactive Scene' Analysis Module (ISAM) developed to provide taskspace database initialization and verification utilizing 3-D graphic overlay modelling, video imaging, and laser radar based range imaging. Through the fusion of taskspace database information and image sensor data, a verifiable taskspace model is generated providing location and orientation data for objects in a task space. This paper also describes applications of the ISAM in the Intelligent Systems Research Laboratory (ISRL) at NASA Langley Research Center, and discusses its performance relative to representation accuracy and operator interface efficiency.

A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion

PubMed Central

Liu, Bailing; Zhang, Fumin; Qu, Xinghua

2015-01-01

An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%∼78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results. PMID:25850067

An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation

PubMed Central

He, Changyu; Kazanzides, Peter; Sen, Hasan Tutkun; Kim, Sungmin; Liu, Yue

2015-01-01

Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions. PMID:26184191

An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation.

PubMed

He, Changyu; Kazanzides, Peter; Sen, Hasan Tutkun; Kim, Sungmin; Liu, Yue

2015-07-08

Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions.

Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

NASA Astrophysics Data System (ADS)

Jiao, Jing; Yue, Jianhai; Pei, Di

2017-10-01

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

ADS-B and multilateration sensor fusion algorithm for air traffic control

NASA Astrophysics Data System (ADS)

Liang, Mengchen

Air traffic is expected to increase rapidly in the next decade. But, the current Air Traffic Control (ATC) system does not meet the demand of the future safety and efficiency. The Next Generation Air Transportation System (NextGen) is a transformation program for the ATC system in the United States. The latest estimates by Federal Aviation Administration (FAA) show that by 2018 NextGen will reduce total delays in flight by 35 percent and provide 23 billion dollars in cumulative benefits. A satellite-based technology called the Automatic Dependent Surveillance-Broadcast (ADS-B) system is one of the most important elements in NextGen. FAA expects that ADS-B systems will be available in the National Airspace System (NAS) by 2020. However, an alternative surveillance system is needed due to vulnerabilities that exist in ADS-B systems. Multilateration has a high accuracy performance and is believed to be an ideal back-up strategy for ADS-B systems. Thus, in this study, we develop the ADS-B and multilateration sensor fusion algorithm for aircraft tracking applications in ATC. The algorithm contains a fault detection function for ADS-B information monitoring by using Trajectory Change Points reports from ADS-B and numerical vectors from a hybrid estimation algorithm. We consider two types of faults in the ADS-B measurement model to show that the algorithm is able to deal with the bad data from ADS-B systems and automatically select good data from multilateration systems. We apply fuzzy logic concepts and generate time variant parameters during the fusion process. The parameters play a role of weights for combining data from different sensors. The algorithm performance is validated through two aircraft tracking examples.

Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas

PubMed Central

Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M.; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T.J.

2010-01-01

In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted. PMID:22294927

Decentralized sensor fusion for Ubiquitous Networking Robotics in Urban Areas.

PubMed

Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T J

2010-01-01

In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

NASA Astrophysics Data System (ADS)

Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

2018-05-01

The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

Sensor fusion methods for reducing false alarms in heart rate monitoring.

PubMed

Borges, Gabriel; Brusamarello, Valner

2016-12-01

Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.

Neuromorphic audio-visual sensor fusion on a sound-localizing robot.

PubMed

Chan, Vincent Yue-Sek; Jin, Craig T; van Schaik, André

2012-01-01

This paper presents the first robotic system featuring audio-visual (AV) sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localization through self motion and visual feedback, using an adaptive ITD-based sound localization algorithm. After training, the robot can localize sound sources (white or pink noise) in a reverberant environment with an RMS error of 4-5° in azimuth. We also investigate the AV source binding problem and an experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. Despite the simplicity of this method and a large number of false visual events in the background, a correct match can be made 75% of the time during the experiment.

Dense range map reconstruction from a versatile robotic sensor system with an active trinocular vision and a passive binocular vision.

PubMed

Kim, Min Young; Lee, Hyunkee; Cho, Hyungsuck

2008-04-10

One major research issue associated with 3D perception by robotic systems is the creation of efficient sensor systems that can generate dense range maps reliably. A visual sensor system for robotic applications is developed that is inherently equipped with two types of sensor, an active trinocular vision and a passive stereo vision. Unlike in conventional active vision systems that use a large number of images with variations of projected patterns for dense range map acquisition or from conventional passive vision systems that work well on specific environments with sufficient feature information, a cooperative bidirectional sensor fusion method for this visual sensor system enables us to acquire a reliable dense range map using active and passive information simultaneously. The fusion algorithms are composed of two parts, one in which the passive stereo vision helps active vision and the other in which the active trinocular vision helps the passive one. The first part matches the laser patterns in stereo laser images with the help of intensity images; the second part utilizes an information fusion technique using the dynamic programming method in which image regions between laser patterns are matched pixel-by-pixel with help of the fusion results obtained in the first part. To determine how the proposed sensor system and fusion algorithms can work in real applications, the sensor system is implemented on a robotic system, and the proposed algorithms are applied. A series of experimental tests is performed for a variety of configurations of robot and environments. The performance of the sensor system is discussed in detail.

Sensor-Data Fusion for Multi-Person Indoor Location Estimation.

PubMed

Mohebbi, Parisa; Stroulia, Eleni; Nikolaidis, Ioanis

2017-10-18

We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other "wearable" device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors' coverage of the monitored space and the quality of the location estimates.

FuzzyFusion: an application architecture for multisource information fusion

NASA Astrophysics Data System (ADS)

Fox, Kevin L.; Henning, Ronda R.

2009-04-01

The correlation of information from disparate sources has long been an issue in data fusion research. Traditional data fusion addresses the correlation of information from sources as diverse as single-purpose sensors to all-source multi-media information. Information system vulnerability information is similar in its diversity of sources and content, and in the desire to draw a meaningful conclusion, namely, the security posture of the system under inspection. FuzzyFusionTM, A data fusion model that is being applied to the computer network operations domain is presented. This model has been successfully prototyped in an applied research environment and represents a next generation assurance tool for system and network security.

Driver drowsiness detection using multimodal sensor fusion

NASA Astrophysics Data System (ADS)

Andreeva, Elena O.; Aarabi, Parham; Philiastides, Marios G.; Mohajer, Keyvan; Emami, Majid

2004-04-01

This paper proposes a multi-modal sensor fusion algorithm for the estimation of driver drowsiness. Driver sleepiness is believed to be responsible for more than 30% of passenger car accidents and for 4% of all accident fatalities. In commercial vehicles, drowsiness is blamed for 58% of single truck accidents and 31% of commercial truck driver fatalities. This work proposes an innovative automatic sleep-onset detection system. Using multiple sensors, the driver"s body is studied as a mechanical structure of springs and dampeners. The sleep-detection system consists of highly sensitive triple-axial accelerometers to monitor the driver"s upper body in 3-D. The subject is modeled as a linear time-variant (LTV) system. An LMS adaptive filter estimation algorithm generates the transfer function (i.e. weight coefficients) for this LTV system. Separate coefficients are generated for the awake and asleep states of the subject. These coefficients are then used to train a neural network. Once trained, the neural network classifies the condition of the driver as either awake or asleep. The system has been tested on a total of 8 subjects. The tests were conducted on sleep-deprived individuals for the sleep state and on fully awake individuals for the awake state. When trained and tested on the same subject, the system detected sleep and awake states of the driver with a success rate of 95%. When the system was trained on three subjects and then retested on a fourth "unseen" subject, the classification rate dropped to 90%. Furthermore, it was attempted to correlate driver posture and sleepiness by observing how car vibrations propagate through a person"s body. Eight additional subjects were studied for this purpose. The results obtained in this experiment proved inconclusive which was attributed to significant differences in the individual habitual postures.

A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion.

PubMed

Yi, Dong-Hoon; Lee, Tae-Jae; Cho, Dong-Il Dan

2018-01-10

In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively.

A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion

PubMed Central

Yi, Dong-Hoon; Lee, Tae-Jae; Cho, Dong-Il “Dan”

2018-01-01

In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively. PMID:29320414

A method based on multi-sensor data fusion for fault detection of planetary gearboxes.

PubMed

Lei, Yaguo; Lin, Jing; He, Zhengjia; Kong, Detong

2012-01-01

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization.

PubMed

Chen, Zhenghua; Zou, Han; Jiang, Hao; Zhu, Qingchang; Soh, Yeng Chai; Xie, Lihua

2015-01-05

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

PubMed Central

Chen, Zhenghua; Zou, Han; Jiang, Hao; Zhu, Qingchang; Soh, Yeng Chai; Xie, Lihua

2015-01-01

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m. PMID:25569750

Method and apparatus for sensor fusion

NASA Technical Reports Server (NTRS)

Krishen, Kumar (Inventor); Shaw, Scott (Inventor); Defigueiredo, Rui J. P. (Inventor)

1991-01-01

Method and apparatus for fusion of data from optical and radar sensors by error minimization procedure is presented. The method was applied to the problem of shape reconstruction of an unknown surface at a distance. The method involves deriving an incomplete surface model from an optical sensor. The unknown characteristics of the surface are represented by some parameter. The correct value of the parameter is computed by iteratively generating theoretical predictions of the radar cross sections (RCS) of the surface, comparing the predicted and the observed values for the RCS, and improving the surface model from results of the comparison. Theoretical RCS may be computed from the surface model in several ways. One RCS prediction technique is the method of moments. The method of moments can be applied to an unknown surface only if some shape information is available from an independent source. The optical image provides the independent information.

Design and Analysis of a Data Fusion Scheme in Mobile Wireless Sensor Networks Based on Multi-Protocol Mobile Agents

PubMed Central

Wu, Chunxue; Wu, Wenliang; Wan, Caihua

2017-01-01

Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications. PMID:29099793

Tier-scalable reconnaissance: the challenge of sensor optimization, sensor deployment, sensor fusion, and sensor interoperability

NASA Astrophysics Data System (ADS)

Fink, Wolfgang; George, Thomas; Tarbell, Mark A.

2007-04-01

Robotic reconnaissance operations are called for in extreme environments, not only those such as space, including planetary atmospheres, surfaces, and subsurfaces, but also in potentially hazardous or inaccessible operational areas on Earth, such as mine fields, battlefield environments, enemy occupied territories, terrorist infiltrated environments, or areas that have been exposed to biochemical agents or radiation. Real time reconnaissance enables the identification and characterization of transient events. A fundamentally new mission concept for tier-scalable reconnaissance of operational areas, originated by Fink et al., is aimed at replacing the engineering and safety constrained mission designs of the past. The tier-scalable paradigm integrates multi-tier (orbit atmosphere surface/subsurface) and multi-agent (satellite UAV/blimp surface/subsurface sensing platforms) hierarchical mission architectures, introducing not only mission redundancy and safety, but also enabling and optimizing intelligent, less constrained, and distributed reconnaissance in real time. Given the mass, size, and power constraints faced by such a multi-platform approach, this is an ideal application scenario for a diverse set of MEMS sensors. To support such mission architectures, a high degree of operational autonomy is required. Essential elements of such operational autonomy are: (1) automatic mapping of an operational area from different vantage points (including vehicle health monitoring); (2) automatic feature extraction and target/region-of-interest identification within the mapped operational area; and (3) automatic target prioritization for close-up examination. These requirements imply the optimal deployment of MEMS sensors and sensor platforms, sensor fusion, and sensor interoperability.

Joint FACET: the Canada-Netherlands initiative to study multisensor data fusion systems

NASA Astrophysics Data System (ADS)

Bosse, Eloi; Theil, Arne; Roy, Jean; Huizing, Albert G.; van Aartsen, Simon

1998-09-01

This paper presents the progress of a collaborative effort between Canada and The Netherlands in analyzing multi-sensor data fusion systems, e.g. for potential application to their respective frigates. In view of the overlapping interest in studying and comparing applicability and performance and advanced state-of-the-art Multi-Sensor Data FUsion (MSDF) techniques, the two research establishments involved have decided to join their efforts in the development of MSDF testbeds. This resulted in the so-called Joint-FACET, a highly modular and flexible series of applications that is capable of processing both real and synthetic input data. Joint-FACET allows the user to create and edit test scenarios with multiple ships, sensor and targets, generate realistic sensor outputs, and to process these outputs with a variety of MSDF algorithms. These MSDF algorithms can also be tested using typical experimental data collected during live military exercises.

Trust Model of Wireless Sensor Networks and Its Application in Data Fusion

PubMed Central

Chen, Zhenguo; Tian, Liqin; Lin, Chuang

2017-01-01

In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced. PMID:28350347

Multi-sensor fusion of Landsat 8 thermal infrared (TIR) and panchromatic (PAN) images.

PubMed

Jung, Hyung-Sup; Park, Sung-Whan

2014-12-18

Data fusion is defined as the combination of data from multiple sensors such that the resulting information is better than would be possible when the sensors are used individually. The multi-sensor fusion of panchromatic (PAN) and thermal infrared (TIR) images is a good example of this data fusion. While a PAN image has higher spatial resolution, a TIR one has lower spatial resolution. In this study, we have proposed an efficient method to fuse Landsat 8 PAN and TIR images using an optimal scaling factor in order to control the trade-off between the spatial details and the thermal information. We have compared the fused images created from different scaling factors and then tested the performance of the proposed method at urban and rural test areas. The test results show that the proposed method merges the spatial resolution of PAN image and the temperature information of TIR image efficiently. The proposed method may be applied to detect lava flows of volcanic activity, radioactive exposure of nuclear power plants, and surface temperature change with respect to land-use change.

Knowledge assistant: A sensor fusion framework for robotic environmental characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feddema, J.T.; Rivera, J.J.; Tucker, S.D.

1996-12-01

A prototype sensor fusion framework called the {open_quotes}Knowledge Assistant{close_quotes} has been developed and tested on a gantry robot at Sandia National Laboratories. This Knowledge Assistant guides the robot operator during the planning, execution, and post analysis stages of the characterization process. During the planning stage, the Knowledge Assistant suggests robot paths and speeds based on knowledge of sensors available and their physical characteristics. During execution, the Knowledge Assistant coordinates the collection of data through a data acquisition {open_quotes}specialist.{close_quotes} During execution and post analysis, the Knowledge Assistant sends raw data to other {open_quotes}specialists,{close_quotes} which include statistical pattern recognition software, a neuralmore » network, and model-based search software. After the specialists return their results, the Knowledge Assistant consolidates the information and returns a report to the robot control system where the sensed objects and their attributes (e.g. estimated dimensions, weight, material composition, etc.) are displayed in the world model. This paper highlights the major components of this system.« less

Data fusion concept in multispectral system for perimeter protection of stationary and moving objects

NASA Astrophysics Data System (ADS)

Ciurapiński, Wieslaw; Dulski, Rafal; Kastek, Mariusz; Szustakowski, Mieczyslaw; Bieszczad, Grzegorz; Życzkowski, Marek; Trzaskawka, Piotr; Piszczek, Marek

2009-09-01

The paper presents the concept of multispectral protection system for perimeter protection for stationary and moving objects. The system consists of active ground radar, thermal and visible cameras. The radar allows the system to locate potential intruders and to control an observation area for system cameras. The multisensor construction of the system ensures significant improvement of detection probability of intruder and reduction of false alarms. A final decision from system is worked out using image data. The method of data fusion used in the system has been presented. The system is working under control of FLIR Nexus system. The Nexus offers complete technology and components to create network-based, high-end integrated systems for security and surveillance applications. Based on unique "plug and play" architecture, system provides unmatched flexibility and simplistic integration of sensors and devices in TCP/IP networks. Using a graphical user interface it is possible to control sensors and monitor streaming video and other data over the network, visualize the results of data fusion process and obtain detailed information about detected intruders over a digital map. System provides high-level applications and operator workload reduction with features such as sensor to sensor cueing from detection devices, automatic e-mail notification and alarm triggering.

Homeland security application of the Army Soft Target Exploitation and Fusion (STEF) system

NASA Astrophysics Data System (ADS)

Antony, Richard T.; Karakowski, Joseph A.

2010-04-01

A fusion system that accommodates both text-based extracted information along with more conventional sensor-derived input has been developed and demonstrated in a terrorist attack scenario as part of the Empire Challenge (EC) 09 Exercise. Although the fusion system was developed to support Army military analysts, the system, based on a set of foundational fusion principles, has direct applicability to department of homeland security (DHS) & defense, law enforcement, and other applications. Several novel fusion technologies and applications were demonstrated in EC09. One such technology is location normalization that accommodates both fuzzy semantic expressions such as behind Library A, across the street from the market place, as well as traditional spatial representations. Additionally, the fusion system provides a range of fusion products not supported by traditional fusion algorithms. Many of these additional capabilities have direct applicability to DHS. A formal test of the fusion system was performed during the EC09 exercise. The system demonstrated that it was able to (1) automatically form tracks, (2) help analysts visualize behavior of individuals over time, (3) link key individuals based on both explicit message-based information as well as discovered (fusion-derived) implicit relationships, and (4) suggest possible individuals of interest based on their association with High Value Individuals (HVI) and user-defined key locations.

Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

PubMed Central

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2012-01-01

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied. PMID:22778629

Improved maturity and ripeness classifications of Magnifera Indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor.

PubMed

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2012-01-01

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks

PubMed Central

Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Zhong, Xionghu

2015-01-01

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones. PMID:26251908

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

PubMed Central

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Sensor-Data Fusion for Multi-Person Indoor Location Estimation

PubMed Central

2017-01-01

We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other “wearable” device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors’ coverage of the monitored space and the quality of the location estimates. PMID:29057812

Fusion of imaging and nonimaging data for surveillance aircraft

NASA Astrophysics Data System (ADS)

Shahbazian, Elisa; Gagnon, Langis; Duquet, Jean Remi; Macieszczak, Maciej; Valin, Pierre

1997-06-01

This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for airborne surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an identification friend or foe (IFF) system, an electronic support measures (ESM) system, a spotlight synthetic aperture radar (SSAR), a forward looking infra-red (FLIR) sensor and a link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (1) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (2) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (3) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as multi-sensor data fusion (MSDF), situation and threat assessment (STA) and resource management (RM).

Design and implementation of PAVEMON: A GIS web-based pavement monitoring system based on large amounts of heterogeneous sensors data

NASA Astrophysics Data System (ADS)

Shahini Shamsabadi, Salar

A web-based PAVEment MONitoring system, PAVEMON, is a GIS oriented platform for accommodating, representing, and leveraging data from a multi-modal mobile sensor system. Stated sensor system consists of acoustic, optical, electromagnetic, and GPS sensors and is capable of producing as much as 1 Terabyte of data per day. Multi-channel raw sensor data (microphone, accelerometer, tire pressure sensor, video) and processed results (road profile, crack density, international roughness index, micro texture depth, etc.) are outputs of this sensor system. By correlating the sensor measurements and positioning data collected in tight time synchronization, PAVEMON attaches a spatial component to all the datasets. These spatially indexed outputs are placed into an Oracle database which integrates seamlessly with PAVEMON's web-based system. The web-based system of PAVEMON consists of two major modules: 1) a GIS module for visualizing and spatial analysis of pavement condition information layers, and 2) a decision-support module for managing maintenance and repair (Mℝ) activities and predicting future budget needs. PAVEMON weaves together sensor data with third-party climate and traffic information from the National Oceanic and Atmospheric Administration (NOAA) and Long Term Pavement Performance (LTPP) databases for an organized data driven approach to conduct pavement management activities. PAVEMON deals with heterogeneous and redundant observations by fusing them for jointly-derived higher-confidence results. A prominent example of the fusion algorithms developed within PAVEMON is a data fusion algorithm used for estimating the overall pavement conditions in terms of ASTM's Pavement Condition Index (PCI). PAVEMON predicts PCI by undertaking a statistical fusion approach and selecting a subset of all the sensor measurements. Other fusion algorithms include noise-removal algorithms to remove false negatives in the sensor data in addition to fusion algorithms developed for

Real-time classification and sensor fusion with a spiking deep belief network

PubMed Central

O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

2013-01-01

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input. PMID:24115919

Real-time classification and sensor fusion with a spiking deep belief network.

PubMed

O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

2013-01-01

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.

Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver.

PubMed

Lee, Chan-Gun; Dao, Nhu-Ngoc; Jang, Seonmin; Kim, Deokhwan; Kim, Yonghun; Cho, Sungrae

2016-06-11

Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3 × 10(-1) to 5.3 × 10(-7), respectively.

Multi Sensor Fusion Using Fitness Adaptive Differential Evolution

NASA Astrophysics Data System (ADS)

Giri, Ritwik; Ghosh, Arnob; Chowdhury, Aritra; Das, Swagatam

The rising popularity of multi-source, multi-sensor networks supports real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on a modified version of Differential Evolution (DE), called Fitness Adaptive Differential Evolution (FiADE). FiADE treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed approach is formulated to produce good result for the problems that are high-dimensional, highly nonlinear, and random. The proposed approach gives better result in case of optimal allocation of sensors. The performance of the proposed approach is compared with an evolutionary algorithm coordination generalized particle model (C-GPM).

An automatic fall detection framework using data fusion of Doppler radar and motion sensor network.

PubMed

Liu, Liang; Popescu, Mihail; Skubic, Marjorie; Rantz, Marilyn

2014-01-01

This paper describes the ongoing work of detecting falls in independent living senior apartments. We have developed a fall detection system with Doppler radar sensor and implemented ceiling radar in real senior apartments. However, the detection accuracy on real world data is affected by false alarms inherent in the real living environment, such as motions from visitors. To solve this issue, this paper proposes an improved framework by fusing the Doppler radar sensor result with a motion sensor network. As a result, performance is significantly improved after the data fusion by discarding the false alarms generated by visitors. The improvement of this new method is tested on one week of continuous data from an actual elderly person who frequently falls while living in her senior home.

Sensor data validation and reconstruction. Phase 1: System architecture study

NASA Technical Reports Server (NTRS)

1991-01-01

The sensor validation and data reconstruction task reviewed relevant literature and selected applicable validation and reconstruction techniques for further study; analyzed the selected techniques and emphasized those which could be used for both validation and reconstruction; analyzed Space Shuttle Main Engine (SSME) hot fire test data to determine statistical and physical relationships between various parameters; developed statistical and empirical correlations between parameters to perform validation and reconstruction tasks, using a computer aided engineering (CAE) package; and conceptually designed an expert system based knowledge fusion tool, which allows the user to relate diverse types of information when validating sensor data. The host hardware for the system is intended to be a Sun SPARCstation, but could be any RISC workstation with a UNIX operating system and a windowing/graphics system such as Motif or Dataviews. The information fusion tool is intended to be developed using the NEXPERT Object expert system shell, and the C programming language.

Sensor fusion of cameras and a laser for city-scale 3D reconstruction.

PubMed

Bok, Yunsu; Choi, Dong-Geol; Kweon, In So

2014-11-04

This paper presents a sensor fusion system of cameras and a 2D laser sensorfor large-scale 3D reconstruction. The proposed system is designed to capture data on afast-moving ground vehicle. The system consists of six cameras and one 2D laser sensor,and they are synchronized by a hardware trigger. Reconstruction of 3D structures is doneby estimating frame-by-frame motion and accumulating vertical laser scans, as in previousworks. However, our approach does not assume near 2D motion, but estimates free motion(including absolute scale) in 3D space using both laser data and image features. In orderto avoid the degeneration associated with typical three-point algorithms, we present a newalgorithm that selects 3D points from two frames captured by multiple cameras. The problemof error accumulation is solved by loop closing, not by GPS. The experimental resultsshow that the estimated path is successfully overlaid on the satellite images, such that thereconstruction result is very accurate.

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction.

PubMed

Cao, Yanpeng; Xu, Baobei; Ye, Zhangyu; Yang, Jiangxin; Cao, Yanlong; Tisse, Christel-Loic; Li, Xin

2018-04-02

Three-dimensional geometrical models with incorporated surface temperature data provide important information for various applications such as medical imaging, energy auditing, and intelligent robots. In this paper we present a robust method for mobile and real-time 3D thermographic reconstruction through depth and thermal sensor fusion. A multimodal imaging device consisting of a thermal camera and a RGB-D sensor is calibrated geometrically and used for data capturing. Based on the underlying principle that temperature information remains robust against illumination and viewpoint changes, we present a Thermal-guided Iterative Closest Point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications. The pose of sensing device is initially estimated using correspondences found through maximizing the thermal consistency between consecutive infrared images. The coarse pose estimate is further refined by finding the motion parameters that minimize a combined geometric and thermographic loss function. Experimental results demonstrate that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction. Through effective fusion of thermal and depth data, the proposed approach generates more accurate 3D thermal models using significantly less scanning data.

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

PubMed Central

Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier

2017-01-01

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm.

PubMed

De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier

2017-10-31

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

Statistical sensor fusion of ECG data using automotive-grade sensors

NASA Astrophysics Data System (ADS)

Koenig, A.; Rehg, T.; Rasshofer, R.

2015-11-01

Driver states such as fatigue, stress, aggression, distraction or even medical emergencies continue to be yield to severe mistakes in driving and promote accidents. A pathway towards improving driver state assessment can be found in psycho-physiological measures to directly quantify the driver's state from physiological recordings. Although heart rate is a well-established physiological variable that reflects cognitive stress, obtaining heart rate contactless and reliably is a challenging task in an automotive environment. Our aim was to investigate, how sensory fusion of two automotive grade sensors would influence the accuracy of automatic classification of cognitive stress levels. We induced cognitive stress in subjects and estimated levels from their heart rate signals, acquired from automotive ready ECG sensors. Using signal quality indices and Kalman filters, we were able to decrease Root Mean Squared Error (RMSE) of heart rate recordings by 10 beats per minute. We then trained a neural network to classify the cognitive workload state of subjects from heart rate and compared classification performance for ground truth, the individual sensors and the fused heart rate signal. We obtained an increase of 5 % higher correct classification by fusing signals as compared to individual sensors, staying only 4 % below the maximally possible classification accuracy from ground truth. These results are a first step towards real world applications of psycho-physiological measurements in vehicle settings. Future implementations of driver state modeling will be able to draw from a larger pool of data sources, such as additional physiological values or vehicle related data, which can be expected to drive classification to significantly higher values.

Context-Aware Personal Navigation Using Embedded Sensor Fusion in Smartphones

PubMed Central

Saeedi, Sara; Moussa, Adel; El-Sheimy, Naser

2014-01-01

Context-awareness is an interesting topic in mobile navigation scenarios where the context of the application is highly dynamic. Using context-aware computing, navigation services consider the situation of user, not only in the design process, but in real time while the device is in use. The basic idea is that mobile navigation services can provide different services based on different contexts—where contexts are related to the user's activity and the device placement. Context-aware systems are concerned with the following challenges which are addressed in this paper: context acquisition, context understanding, and context-aware application adaptation. The proposed approach in this paper is using low-cost sensors in a multi-level fusion scheme to improve the accuracy and robustness of context-aware navigation system. The experimental results demonstrate the capabilities of the context-aware Personal Navigation Systems (PNS) for outdoor personal navigation using a smartphone. PMID:24670715

Context-aware personal navigation using embedded sensor fusion in smartphones.

PubMed

Saeedi, Sara; Moussa, Adel; El-Sheimy, Naser

2014-03-25

Context-awareness is an interesting topic in mobile navigation scenarios where the context of the application is highly dynamic. Using context-aware computing, navigation services consider the situation of user, not only in the design process, but in real time while the device is in use. The basic idea is that mobile navigation services can provide different services based on different contexts-where contexts are related to the user's activity and the device placement. Context-aware systems are concerned with the following challenges which are addressed in this paper: context acquisition, context understanding, and context-aware application adaptation. The proposed approach in this paper is using low-cost sensors in a multi-level fusion scheme to improve the accuracy and robustness of context-aware navigation system. The experimental results demonstrate the capabilities of the context-aware Personal Navigation Systems (PNS) for outdoor personal navigation using a smartphone.

Fusion of a FBG-based health monitoring system for wind turbines with a fiber-optic lightning detection system

NASA Astrophysics Data System (ADS)

Krämer, Sebastian G. M.; Wiesent, Benjamin; Müller, Mathias S.; Puente León, Fernando; Méndez Hernández, Yarú

2008-04-01

Wind turbine blades are made of composite materials and reach a length of more than 42 meters. Developments for modern offshore turbines are working on about 60 meters long blades. Hence, with the increasing height of the turbines and the remote locations of the structures, health monitoring systems are becoming more and more important. Therefore, fiber-optic sensor systems are well-suited, as they are lightweight, immune against electromagnetic interference (EMI), and as they can be multiplexed. Based on two separately existing concepts for strain measurements and lightning detection on wind turbines, a fused system is presented. The strain measurement system is based on a reflective fiber-Bragg-grating (FBG) network embedded in the composite structure of the blade. For lightning detection, transmissive &fiber-optic magnetic field sensors based on the Faraday effect are used to register the lightning parameters and estimate the impact point. Hence, an existing lightning detection system will be augmented, due to the fusion, by the capability to measure strain, temperature and vibration. Load, strain, temperature and impact detection information can be incorporated into the turbine's monitoring or SCADA system and remote controlled by operators. Data analysis techniques allow dynamic maintenance scheduling to become a reality, what is of special interest for the cost-effective maintenance of large offshore or badly attainable onshore wind parks. To prove the feasibility of this sensor fusion on one optical fiber, interferences between both sensor systems are investigated and evaluated.

Multisensor data fusion for IED threat detection

NASA Astrophysics Data System (ADS)

Mees, Wim; Heremans, Roel

2012-10-01

In this paper we present the multi-sensor registration and fusion algorithms that were developed for a force protection research project in order to detect threats against military patrol vehicles. The fusion is performed at object level, using a hierarchical evidence aggregation approach. It first uses expert domain knowledge about the features used to characterize the detected threats, that is implemented in the form of a fuzzy expert system. The next level consists in fusing intra-sensor and inter-sensor information. Here an ordered weighted averaging operator is used. The object level fusion between candidate threats that are detected asynchronously on a moving vehicle by sensors with different imaging geometries, requires an accurate sensor to world coordinate transformation. This image registration will also be discussed in this paper.

A sensor fusion method for tracking vertical velocity and height based on inertial and barometric altimeter measurements.

PubMed

Sabatini, Angelo Maria; Genovese, Vincenzo

2014-07-24

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04-0.24 m/s; height RMSE was in the range 5-68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions.

Evolution of an Intelligent Information Fusion System

NASA Technical Reports Server (NTRS)

Campbell, William J.; Cromp, Robert F.

1990-01-01

Consideration is given to the hardware and software needed to manage the enormous amount and complexity of data that the next generation of space-borne sensors will provide. An anthology is presented illustrating the evolution of artificial intelligence, science data processing, and management from the 1960s to the near future. Problems and limitations of technologies, data structures, data standards, and conceptual thinking are addressed. The development of an end-to-end Intelligent Information Fusion System that embodies knowledge of the user's domain-specific goals is proposed.

Sensor Fusion of Gaussian Mixtures for Ballistic Target Tracking in the Re-Entry Phase.

PubMed

Lu, Kelin; Zhou, Rui

2016-08-15

A sensor fusion methodology for the Gaussian mixtures model is proposed for ballistic target tracking with unknown ballistic coefficients. To improve the estimation accuracy, a track-to-track fusion architecture is proposed to fuse tracks provided by the local interacting multiple model filters. During the fusion process, the duplicate information is removed by considering the first order redundant information between the local tracks. With extensive simulations, we show that the proposed algorithm improves the tracking accuracy in ballistic target tracking in the re-entry phase applications.

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

PubMed Central

Li, Chao; Zhang, Zhenjiang; Chao, Han-Chieh

2017-01-01

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. PMID:29280950

Sensor fusion and augmented reality with the SAFIRE system

NASA Astrophysics Data System (ADS)

Saponaro, Philip; Treible, Wayne; Phelan, Brian; Sherbondy, Kelly; Kambhamettu, Chandra

2018-04-01

The Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) mobile radar system was developed and exercised at an arid U.S. test site. The system can detect hidden target using radar, a global positioning system (GPS), dual stereo color cameras, and dual stereo thermal cameras. An Augmented Reality (AR) software interface allows the user to see a single fused video stream containing the SAR, color, and thermal imagery. The stereo sensors allow the AR system to display both fused 2D imagery and 3D metric reconstructions, where the user can "fly" around the 3D model and switch between the modalities.

Data fusion: principles and applications in air defense

NASA Astrophysics Data System (ADS)

Maltese, Dominique; Lucas, Andre

1998-07-01

Within a Surveillance and Reconnaissance System, the Fusion Process is an essential part of the software package since the different sensors measurements are combined by this process; each sensor sends its data to a fusion center whose task is to elaborate the best tactical situation. In this paper, a practical algorithm of data fusion applied to a military application context is presented; the case studied here is a medium-range surveillance situation featuring a dual-sensor platform which combines a surveillance Radar and an IRST; both sensors are collocated. The presented performances were obtained on validation scenarios via simulations performed by SAGEM with the ESSOR ('Environnement de Simulation de Senseurs Optroniques et Radar') multisensor simulation test bench.

Neuromechanical sensor fusion yields highest accuracies in predicting ambulation mode transitions for trans-tibial amputees.

PubMed

Tkach, D C; Hargrove, L J

2013-01-01

Advances in battery and actuator technology have enabled clinical use of powered lower limb prostheses such as the BiOM Powered Ankle. To allow ambulation over various types of terrains, such devices rely on built-in mechanical sensors or manual actuation by the amputee to transition into an operational mode that is suitable for a given terrain. It is unclear if mechanical sensors alone can accurately modulate operational modes while voluntary actuation prevents seamless, naturalistic gait. Ensuring that the prosthesis is ready to accommodate new terrain types at first step is critical for user safety. EMG signals from patient's residual leg muscles may provide additional information to accurately choose the proper mode of prosthesis operation. Using a pattern recognition classifier we compared the accuracy of predicting 8 different mode transitions based on (1) prosthesis mechanical sensor output (2) EMG recorded from residual limb and (3) fusion of EMG and mechanical sensor data. Our findings indicate that the neuromechanical sensor fusion significantly decreases errors in predicting 10 mode transitions as compared to using either mechanical sensors or EMG alone (2.3±0.7% vs. 7.8±0.9% and 20.2±2.0% respectively).

A Sensor Fusion Method for Tracking Vertical Velocity and Height Based on Inertial and Barometric Altimeter Measurements

PubMed Central

Sabatini, Angelo Maria; Genovese, Vincenzo

2014-01-01

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04–0.24 m/s; height RMSE was in the range 5–68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions. PMID:25061835

Sensor Fusion of Gaussian Mixtures for Ballistic Target Tracking in the Re-Entry Phase

PubMed Central

Lu, Kelin; Zhou, Rui

2016-01-01

A sensor fusion methodology for the Gaussian mixtures model is proposed for ballistic target tracking with unknown ballistic coefficients. To improve the estimation accuracy, a track-to-track fusion architecture is proposed to fuse tracks provided by the local interacting multiple model filters. During the fusion process, the duplicate information is removed by considering the first order redundant information between the local tracks. With extensive simulations, we show that the proposed algorithm improves the tracking accuracy in ballistic target tracking in the re-entry phase applications. PMID:27537883

An Extension to Deng's Entropy in the Open World Assumption with an Application in Sensor Data Fusion.

PubMed

Tang, Yongchuan; Zhou, Deyun; Chan, Felix T S

2018-06-11

Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed world where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the open world by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the open world assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed world wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few open issues still exist in the current work: the necessary properties for a belief entropy in the open world assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty.

Flexible Fusion Structure-Based Performance Optimization Learning for Multisensor Target Tracking

PubMed Central

Ge, Quanbo; Wei, Zhongliang; Cheng, Tianfa; Chen, Shaodong; Wang, Xiangfeng

2017-01-01

Compared with the fixed fusion structure, the flexible fusion structure with mixed fusion methods has better adjustment performance for the complex air task network systems, and it can effectively help the system to achieve the goal under the given constraints. Because of the time-varying situation of the task network system induced by moving nodes and non-cooperative target, and limitations such as communication bandwidth and measurement distance, it is necessary to dynamically adjust the system fusion structure including sensors and fusion methods in a given adjustment period. Aiming at this, this paper studies the design of a flexible fusion algorithm by using an optimization learning technology. The purpose is to dynamically determine the sensors’ numbers and the associated sensors to take part in the centralized and distributed fusion processes, respectively, herein termed sensor subsets selection. Firstly, two system performance indexes are introduced. Especially, the survivability index is presented and defined. Secondly, based on the two indexes and considering other conditions such as communication bandwidth and measurement distance, optimization models for both single target tracking and multi-target tracking are established. Correspondingly, solution steps are given for the two optimization models in detail. Simulation examples are demonstrated to validate the proposed algorithms. PMID:28481243

Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances

PubMed Central

Liu, Baoyu; Zhan, Xingqun; Zhu, Zheng H.

2017-01-01

As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different estimate pairing methods are presented and analyzed. In order to assess the influence of fusion structure on fusion performance, two fusion performance assessment parameters are defined as Fusion Distance and Fusion Index. Moreover, the formula for calculating the upper bounds of actual fused error covariances of the presented multisensor LE fusers is also provided. Demonstrated with simulation examples, the Fusion Index indicates fuser’s actual fused accuracy and its sensitivity to the sensor orders, as well as its robustness to the accuracy of newly added sensors. Compared to the LE fuser with sequential structure, the LE fusers with proposed parallel structures not only significantly improve their properties in these aspects, but also embrace better performances in consistency and computation efficiency. The presented multisensor LE fusers generally have better accuracies than covariance intersection (CI) fusion algorithm and are consistent when the local estimates are weakly correlated. PMID:28661442

A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation

PubMed Central

Li, Yun

2017-01-01

We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss–Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter (PF), a weighted measurement fusion PF (WMF-PF) is presented. The accuracy of WMF-PF appears good and has a lower computational cost when compared to centralized fusion PF (CF-PF). An example is given to show the effectiveness of the proposed algorithms. PMID:28956862

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.

PubMed

Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia

2018-06-12

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Neuromorphic vision sensors and preprocessors in system applications

NASA Astrophysics Data System (ADS)

Kramer, Joerg; Indiveri, Giacomo

1998-09-01

A partial review of neuromorphic vision sensors that are suitable for use in autonomous systems is presented. Interfaces are being developed to multiplex the high- dimensional output signals of arrays of such sensors and to communicate them in standard formats to off-chip devices for higher-level processing, actuation, storage and display. Alternatively, on-chip processing stages may be implemented to extract sparse image parameters, thereby obviating the need for multiplexing. Autonomous robots are used to test neuromorphic vision chips in real-world environments and to explore the possibilities of data fusion from different sensing modalities. Examples of autonomous mobile systems that use neuromorphic vision chips for line tracking and optical flow matching are described.

Data Fusion for a Vision-Radiological System for Source Tracking and Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Enqvist, Andreas; Koppal, Sanjeev

2015-07-01

A multidisciplinary approach to allow the tracking of the movement of radioactive sources by fusing data from multiple radiological and visual sensors is under development. The goal is to improve the ability to detect, locate, track and identify nuclear/radiological threats. The key concept is that such widely available visual and depth sensors can impact radiological detection, since the intensity fall-off in the count rate can be correlated to movement in three dimensions. To enable this, we pose an important question; what is the right combination of sensing modalities and vision algorithms that can best compliment a radiological sensor, for themore » purpose of detection and tracking of radioactive material? Similarly what is the best radiation detection methods and unfolding algorithms suited for data fusion with tracking data? Data fusion of multi-sensor data for radiation detection have seen some interesting developments lately. Significant examples include intelligent radiation sensor systems (IRSS), which are based on larger numbers of distributed similar or identical radiation sensors coupled with position data for network capable to detect and locate radiation source. Other developments are gamma-ray imaging systems based on Compton scatter in segmented detector arrays. Similar developments using coded apertures or scatter cameras for neutrons have recently occurred. The main limitation of such systems is not so much in their capability but rather in their complexity and cost which is prohibitive for large scale deployment. Presented here is a fusion system based on simple, low-cost computer vision and radiological sensors for tracking of multiple objects and identifying potential radiological materials being transported or shipped. The main focus of this work is the development on two separate calibration algorithms for characterizing the fused sensor system. The deviation from a simple inverse square-root fall-off of radiation intensity is explored

A Smartphone-Based Driver Safety Monitoring System Using Data Fusion

PubMed Central

Lee, Boon-Giin; Chung, Wan-Young

2012-01-01

This paper proposes a method for monitoring driver safety levels using a data fusion approach based on several discrete data types: eye features, bio-signal variation, in-vehicle temperature, and vehicle speed. The driver safety monitoring system was developed in practice in the form of an application for an Android-based smartphone device, where measuring safety-related data requires no extra monetary expenditure or equipment. Moreover, the system provides high resolution and flexibility. The safety monitoring process involves the fusion of attributes gathered from different sensors, including video, electrocardiography, photoplethysmography, temperature, and a three-axis accelerometer, that are assigned as input variables to an inference analysis framework. A Fuzzy Bayesian framework is designed to indicate the driver’s capability level and is updated continuously in real-time. The sensory data are transmitted via Bluetooth communication to the smartphone device. A fake incoming call warning service alerts the driver if his or her safety level is suspiciously compromised. Realistic testing of the system demonstrates the practical benefits of multiple features and their fusion in providing a more authentic and effective driver safety monitoring. PMID:23247416

Multisensor data fusion for integrated maritime surveillance

NASA Astrophysics Data System (ADS)

Premji, A.; Ponsford, A. M.

1995-01-01

A prototype Integrated Coastal Surveillance system has been developed on Canada's East Coast to provide effective surveillance out to and beyond the 200 nautical mile Exclusive Economic Zone. The system has been designed to protect Canada's natural resources, and to monitor and control the coastline for smuggling, drug trafficking, and similar illegal activity. This paper describes the Multiple Sensor - Multiple Target data fusion system that has been developed. The fusion processor has been developed around the celebrated Multiple Hypothesis Tracking algorithm which accommodates multiple targets, new targets, false alarms, and missed detections. This processor performs four major functions: plot-to-track association to form individual radar tracks; fusion of radar tracks with secondary sensor reports; track identification and tagging using secondary reports; and track level fusion to form common tracks. Radar data from coherent and non-coherent radars has been used to evaluate the performance of the processor. This paper presents preliminary results.

Review of Fusion Systems and Contributing Technologies for SIHS-TD (Examen des Systemes de Fusion et des Technologies d’Appui pour la DT SIHS)

DTIC Science & Technology

2007-03-31

Unlimited, Nivisys, Insight technology, Elcan, FLIR Systems, Stanford photonics Hardware Sensor fusion processors Video processing boards Image, video...Engineering The SPIE Digital Library is a resource for optics and photonics information. It contains more than 70,000 full-text papers from SPIE...conditions Top row: Stanford Photonics XR-Mega-10 Extreme 1400 x 1024 pixels ICCD detector, 33 msec exposure, no binning. Middle row: Andor EEV iXon

Data fusion for a vision-aided radiological detection system: Calibration algorithm performance

NASA Astrophysics Data System (ADS)

Stadnikia, Kelsey; Henderson, Kristofer; Martin, Allan; Riley, Phillip; Koppal, Sanjeev; Enqvist, Andreas

2018-05-01

In order to improve the ability to detect, locate, track and identify nuclear/radiological threats, the University of Florida nuclear detection community has teamed up with the 3D vision community to collaborate on a low cost data fusion system. The key is to develop an algorithm to fuse the data from multiple radiological and 3D vision sensors as one system. The system under development at the University of Florida is being assessed with various types of radiological detectors and widely available visual sensors. A series of experiments were devised utilizing two EJ-309 liquid organic scintillation detectors (one primary and one secondary), a Microsoft Kinect for Windows v2 sensor and a Velodyne HDL-32E High Definition LiDAR Sensor which is a highly sensitive vision sensor primarily used to generate data for self-driving cars. Each experiment consisted of 27 static measurements of a source arranged in a cube with three different distances in each dimension. The source used was Cf-252. The calibration algorithm developed is utilized to calibrate the relative 3D-location of the two different types of sensors without need to measure it by hand; thus, preventing operator manipulation and human errors. The algorithm can also account for the facility dependent deviation from ideal data fusion correlation. Use of the vision sensor to determine the location of a sensor would also limit the possible locations and it does not allow for room dependence (facility dependent deviation) to generate a detector pseudo-location to be used for data analysis later. Using manually measured source location data, our algorithm-predicted the offset detector location within an average of 20 cm calibration-difference to its actual location. Calibration-difference is the Euclidean distance from the algorithm predicted detector location to the measured detector location. The Kinect vision sensor data produced an average calibration-difference of 35 cm and the HDL-32E produced an average

Decision-level fusion of SAR and IR sensor information for automatic target detection

NASA Astrophysics Data System (ADS)

Cho, Young-Rae; Yim, Sung-Hyuk; Cho, Hyun-Woong; Won, Jin-Ju; Song, Woo-Jin; Kim, So-Hyeon

2017-05-01

We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR) sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief assignment is used to transform this map into a belief map. The detection results of sensors are combined to build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.

Multisensor Fusion for Change Detection

NASA Astrophysics Data System (ADS)

Schenk, T.; Csatho, B.

2005-12-01

Combining sensors that record different properties of a 3-D scene leads to complementary and redundant information. If fused properly, a more robust and complete scene description becomes available. Moreover, fusion facilitates automatic procedures for object reconstruction and modeling. For example, aerial imaging sensors, hyperspectral scanning systems, and airborne laser scanning systems generate complementary data. We describe how data from these sensors can be fused for such diverse applications as mapping surface erosion and landslides, reconstructing urban scenes, monitoring urban land use and urban sprawl, and deriving velocities and surface changes of glaciers and ice sheets. An absolute prerequisite for successful fusion is a rigorous co-registration of the sensors involved. We establish a common 3-D reference frame by using sensor invariant features. Such features are caused by the same object space phenomena and are extracted in multiple steps from the individual sensors. After extracting, segmenting and grouping the features into more abstract entities, we discuss ways on how to automatically establish correspondences. This is followed by a brief description of rigorous mathematical models suitable to deal with linear and area features. In contrast to traditional, point-based registration methods, lineal and areal features lend themselves to a more robust and more accurate registration. More important, the chances to automate the registration process increases significantly. The result of the co-registration of the sensors is a unique transformation between the individual sensors and the object space. This makes spatial reasoning of extracted information more versatile; reasoning can be performed in sensor space or in 3-D space where domain knowledge about features and objects constrains reasoning processes, reduces the search space, and helps to make the problem well-posed. We demonstrate the feasibility of the proposed multisensor fusion approach

Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2014-09-02

Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers. Copyright © 2014 Elsevier B.V. All rights reserved.

Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots.

PubMed

Zhang, Xinzheng; Rad, Ahmad B; Wong, Yiu-Kwong

2012-01-01

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

A dual-channel fusion system of visual and infrared images based on color transfer

NASA Astrophysics Data System (ADS)

Pei, Chuang; Jiang, Xiao-yu; Zhang, Peng-wei; Liang, Hao-cong

2013-09-01

A dual-channel fusion system of visual and infrared images based on color transfer The increasing availability and deployment of imaging sensors operating in multiple spectrums has led to a large research effort in image fusion, resulting in a plethora of pixel-level image fusion algorithms. However, most of these algorithms have gray or false color fusion results which are not adapt to human vision. Transfer color from a day-time reference image to get natural color fusion result is an effective way to solve this problem, but the computation cost of color transfer is expensive and can't meet the request of real-time image processing. We developed a dual-channel infrared and visual images fusion system based on TMS320DM642 digital signal processing chip. The system is divided into image acquisition and registration unit, image fusion processing unit, system control unit and image fusion result out-put unit. The image registration of dual-channel images is realized by combining hardware and software methods in the system. False color image fusion algorithm in RGB color space is used to get R-G fused image, then the system chooses a reference image to transfer color to the fusion result. A color lookup table based on statistical properties of images is proposed to solve the complexity computation problem in color transfer. The mapping calculation between the standard lookup table and the improved color lookup table is simple and only once for a fixed scene. The real-time fusion and natural colorization of infrared and visual images are realized by this system. The experimental result shows that the color-transferred images have a natural color perception to human eyes, and can highlight the targets effectively with clear background details. Human observers with this system will be able to interpret the image better and faster, thereby improving situational awareness and reducing target detection time.

Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots.

PubMed

Marín, Leonardo; Vallés, Marina; Soriano, Ángel; Valera, Ángel; Albertos, Pedro

2013-10-21

This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments.

Statistical sensor fusion analysis of near-IR polarimetric and thermal imagery for the detection of minelike targets

NASA Astrophysics Data System (ADS)

Weisenseel, Robert A.; Karl, William C.; Castanon, David A.; DiMarzio, Charles A.

1999-02-01

We present an analysis of statistical model based data-level fusion for near-IR polarimetric and thermal data, particularly for the detection of mines and mine-like targets. Typical detection-level data fusion methods, approaches that fuse detections from individual sensors rather than fusing at the level of the raw data, do not account rationally for the relative reliability of different sensors, nor the redundancy often inherent in multiple sensors. Representative examples of such detection-level techniques include logical AND/OR operations on detections from individual sensors and majority vote methods. In this work, we exploit a statistical data model for the detection of mines and mine-like targets to compare and fuse multiple sensor channels. Our purpose is to quantify the amount of knowledge that each polarimetric or thermal channel supplies to the detection process. With this information, we can make reasonable decisions about the usefulness of each channel. We can use this information to improve the detection process, or we can use it to reduce the number of required channels.

Multisensor fusion with non-optimal decision rules: the challenges of open world sensing

NASA Astrophysics Data System (ADS)

Minor, Christian; Johnson, Kevin

2014-05-01

In this work, simple, generic models of chemical sensing are used to simulate sensor array data and to illustrate the impact on overall system performance that specific design choices impart. The ability of multisensor systems to perform multianalyte detection (i.e., distinguish multiple targets) is explored by examining the distinction between fundamental design-related limitations stemming from mismatching of mixture composition to fused sensor measurement spaces, and limitations that arise from measurement uncertainty. Insight on the limits and potential of sensor fusion to robustly address detection tasks in realistic field conditions can be gained through an examination of a) the underlying geometry of both the composition space of sources one hopes to elucidate and the measurement space a fused sensor system is capable of generating, and b) the informational impact of uncertainty on both of these spaces. For instance, what is the potential impact on sensor fusion in an open world scenario where unknown interferants may contaminate target signals? Under complex and dynamic backgrounds, decision rules may implicitly become non-optimal and adding sensors may increase the amount of conflicting information observed. This suggests that the manner in which a decision rule handles sensor conflict can be critical in leveraging sensor fusion for effective open world sensing, and becomes exponentially more important as more sensors are added. Results and design considerations for handling conflicting evidence in Bayes and Dempster-Shafer fusion frameworks are presented. Bayesian decision theory is used to provide an upper limit on detector performance of simulated sensor systems.

Robust sensor fusion of unobtrusively measured heart rate.

PubMed

Wartzek, Tobias; Brüser, Christoph; Walter, Marian; Leonhardt, Steffen

2014-03-01

Contactless vital sign measurement technologies often have the drawback of severe motion artifacts and periods in which no signal is available. However, using several identical or physically different sensors, redundancy can be used to decrease the error in noncontact heart rate estimation, while increasing the time period during which reliable data are available. In this paper, we show for the first time two major results in case of contactless heart rate measurements deduced from a capacitive ECG and optical pulse signals. First, an artifact detection is an essential preprocessing step to allow a reliable fusion. Second, the robust but computationally efficient median already provides good results; however, using a Bayesian approach, and a short time estimation of the variance, best results in terms of difference to reference heart rate and temporal coverage can be achieved. In this paper, six sensor signals were used and coverage increased from 0-90% to 80-94%, while the difference between the estimated heart rate and the gold standard was less than ±2 BPM.

The Radio Frequency Health Node Wireless Sensor System

NASA Technical Reports Server (NTRS)

Valencia, J. Emilio; Stanley, Priscilla C.; Mackey, Paul J.

2009-01-01

The Radio Frequency Health Node (RFHN) wireless sensor system differs from other wireless sensor systems in ways originally intended to enhance utility as an instrumentation system for a spacecraft. The RFHN can also be adapted to use in terrestrial applications in which there are requirements for operational flexibility and integrability into higher-level instrumentation and data acquisition systems. As shown in the figure, the heart of the system is the RFHN, which is a unit that passes commands and data between (1) one or more commercially available wireless sensor units (optionally, also including wired sensor units) and (2) command and data interfaces with a local control computer that may be part of the spacecraft or other engineering system in which the wireless sensor system is installed. In turn, the local control computer can be in radio or wire communication with a remote control computer that may be part of a higher-level system. The remote control computer, acting via the local control computer and the RFHN, cannot only monitor readout data from the sensor units but can also remotely configure (program or reprogram) the RFHN and the sensor units during operation. In a spacecraft application, the RFHN and the sensor units can also be configured more nearly directly, prior to launch, via a serial interface that includes an umbilical cable between the spacecraft and ground support equipment. In either case, the RFHN wireless sensor system has the flexibility to be configured, as required, with different numbers and types of sensors for different applications. The RFHN can be used to effect realtime transfer of data from, and commands to, the wireless sensor units. It can also store data for later retrieval by an external computer. The RFHN communicates with the wireless sensor units via a radio transceiver module. The modular design of the RFHN makes it possible to add radio transceiver modules as needed to accommodate additional sets of wireless sensor

A Navigation System for the Visually Impaired: A Fusion of Vision and Depth Sensor

PubMed Central

Kanwal, Nadia; Bostanci, Erkan; Currie, Keith; Clark, Adrian F.

2015-01-01

For a number of years, scientists have been trying to develop aids that can make visually impaired people more independent and aware of their surroundings. Computer-based automatic navigation tools are one example of this, motivated by the increasing miniaturization of electronics and the improvement in processing power and sensing capabilities. This paper presents a complete navigation system based on low cost and physically unobtrusive sensors such as a camera and an infrared sensor. The system is based around corners and depth values from Kinect's infrared sensor. Obstacles are found in images from a camera using corner detection, while input from the depth sensor provides the corresponding distance. The combination is both efficient and robust. The system not only identifies hurdles but also suggests a safe path (if available) to the left or right side and tells the user to stop, move left, or move right. The system has been tested in real time by both blindfolded and blind people at different indoor and outdoor locations, demonstrating that it operates adequately. PMID:27057135

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

PubMed Central

Elfring, Jos; Appeldoorn, Rein; van den Dries, Sjoerd; Kwakkernaat, Maurice

2016-01-01

The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture. PMID:27727171

Monitoring of slope-instabilities and deformations with Micro-Electro-Mechanical-Systems (MEMS) in wireless ad-hoc Sensor Networks

NASA Astrophysics Data System (ADS)

Arnhardt, C.; Fernández-Steeger, T. M.; Azzam, R.

2009-04-01

efficiency that permits measurements over a long period of time. A special sensor-board that accommodates the measuring sensors and the node of the WSN was developed. The standardized interfaces of the measuring sensors permit an easy interaction with the node and thus enable an uncomplicated data transfer to the gateway. The 3-axis acceleration sensor (measuring range: +/- 2g), the 2-axis inclination sensor (measuring range: +/- 30°) for measuring tilt and the barometric pressure sensor (measuring rang: 30kPa - 120 kPa) for measuring sub-meter height changes (altimeter) are currently integrated into the sensor network and are tested in realistic experiments. In addition sensor nodes with precise potentiometric displacement and linear magnetorestrictive position transducer are used for extension and convergence measurements. According to the accuracy of the first developed test stations, the results of the experiments showed that the selected sensors meet the requirement profile, as the stability is satisfying and the spreading of the data is quite low. Therefore the jet developed sensor boards can be tested in a larger environment of a sensor network. In order to get more information about accuracy in detail, experiments in a new more precise test bed and tests with different sampling rates will follow. Another increasingly important aspect for the future is the fusion of sensor data (i.e. combination and comparison) to identify malfunctions and to reduce false alarm rates, while increasing data quality at the same time. The correlation of different (complementary sensor fusion) but also identical sensor-types (redundant sensor fusion) permits a validation of measuring data. The development of special algorithms allows in a further step to analyze and evaluate the data from all nodes of the network together (sensor node fusion). The sensor fusion contributes to the decision making of alarm and early warning systems and allows a better interpretation of data. The network

High Level Information Fusion (HLIF) with nested fusion loops

NASA Astrophysics Data System (ADS)

Woodley, Robert; Gosnell, Michael; Fischer, Amber

2013-05-01

Situation modeling and threat prediction require higher levels of data fusion in order to provide actionable information. Beyond the sensor data and sources the analyst has access to, the use of out-sourced and re-sourced data is becoming common. Through the years, some common frameworks have emerged for dealing with information fusion—perhaps the most ubiquitous being the JDL Data Fusion Group and their initial 4-level data fusion model. Since these initial developments, numerous models of information fusion have emerged, hoping to better capture the human-centric process of data analyses within a machine-centric framework. 21st Century Systems, Inc. has developed Fusion with Uncertainty Reasoning using Nested Assessment Characterizer Elements (FURNACE) to address challenges of high level information fusion and handle bias, ambiguity, and uncertainty (BAU) for Situation Modeling, Threat Modeling, and Threat Prediction. It combines JDL fusion levels with nested fusion loops and state-of-the-art data reasoning. Initial research has shown that FURNACE is able to reduce BAU and improve the fusion process by allowing high level information fusion (HLIF) to affect lower levels without the double counting of information or other biasing issues. The initial FURNACE project was focused on the underlying algorithms to produce a fusion system able to handle BAU and repurposed data in a cohesive manner. FURNACE supports analyst's efforts to develop situation models, threat models, and threat predictions to increase situational awareness of the battlespace. FURNACE will not only revolutionize the military intelligence realm, but also benefit the larger homeland defense, law enforcement, and business intelligence markets.

An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis

PubMed Central

Zhou, Deyun; Zhuang, Miaoyan; Fang, Xueyi; Xie, Chunhe

2017-01-01

As an important tool of information fusion, Dempster–Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster–Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster’s combination rule. The proposed method has a better performance in conflict management and fault diagnosis due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in fault diagnosis are presented to show the rationality and efficiency of the proposed method. PMID:28927017

Wrap-Around Out-the-Window Sensor Fusion System

NASA Technical Reports Server (NTRS)

Fox, Jeffrey; Boe, Eric A.; Delgado, Francisco; Secor, James B.; Clark, Michael R.; Ehlinger, Kevin D.; Abernathy, Michael F.

2009-01-01

The Advanced Cockpit Evaluation System (ACES) includes communication, computing, and display subsystems, mounted in a van, that synthesize out-the-window views to approximate the views of the outside world as it would be seen from the cockpit of a crewed spacecraft, aircraft, or remote control of a ground vehicle or UAV (unmanned aerial vehicle). The system includes five flat-panel display units arranged approximately in a semicircle around an operator, like cockpit windows. The scene displayed on each panel represents the view through the corresponding cockpit window. Each display unit is driven by a personal computer equipped with a video-capture card that accepts live input from any of a variety of sensors (typically, visible and/or infrared video cameras). Software running in the computers blends the live video images with synthetic images that could be generated, for example, from heads-up-display outputs, waypoints, corridors, or from satellite photographs of the same geographic region. Data from a Global Positioning System receiver and an inertial navigation system aboard the remote vehicle are used by the ACES software to keep the synthetic and live views in registration. If the live image were to fail, the synthetic scenes could still be displayed to maintain situational awareness.

Panel summary of cyber-physical systems (CPS) and Internet of Things (IoT) opportunities with information fusion

NASA Astrophysics Data System (ADS)

Blasch, Erik; Kadar, Ivan; Grewe, Lynne L.; Brooks, Richard; Yu, Wei; Kwasinski, Andres; Thomopoulos, Stelios; Salerno, John; Qi, Hairong

2017-05-01

During the 2016 SPIE DSS conference, nine panelists were invited to highlight the trends and opportunities in cyber-physical systems (CPS) and Internet of Things (IoT) with information fusion. The world will be ubiquitously outfitted with many sensors to support our daily living thorough the Internet of Things (IoT), manage infrastructure developments with cyber-physical systems (CPS), as well as provide communication through networked information fusion technology over the internet (NIFTI). This paper summarizes the panel discussions on opportunities of information fusion to the growing trends in CPS and IoT. The summary includes the concepts and areas where information supports these CPS/IoT which includes situation awareness, transportation, and smart grids.

Multisensor fusion for 3D target tracking using track-before-detect particle filter

NASA Astrophysics Data System (ADS)

Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.

2015-05-01

This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.

Fusion of radar and ultrasound sensors for concealed weapons detection

NASA Astrophysics Data System (ADS)

Felber, Franklin S.; Davis, Herbert T., III; Mallon, Charles E.; Wild, Norbert C.

1996-06-01

An integrated radar and ultrasound sensor, capable of remotely detecting and imaging concealed weapons, is being developed. A modified frequency-agile, mine-detection radar is intended to specify with high probability of detection at ranges of 1 to 10 m which individuals in a moving crowd may be concealing metallic or nonmetallic weapons. Within about 1 to 5 m, the active ultrasound sensor is intended to enable a user to identify a concealed weapon on a moving person with low false-detection rate, achieved through a real-time centimeter-resolution image of the weapon. The goal for sensor fusion is to have the radar acquire concealed weapons at long ranges and seamlessly hand over tracking data to the ultrasound sensor for high-resolution imaging on a video monitor. We have demonstrated centimeter-resolution ultrasound images of metallic and non-metallic weapons concealed on a human at ranges over 1 m. Processing of the ultrasound images includes filters for noise, frequency, brightness, and contrast. A frequency-agile radar has been developed by JAYCOR under the U.S. Army Advanced Mine Detection Radar Program. The signature of an armed person, detected by this radar, differs appreciably from that of the same person unarmed.

A multi-sensor land mine detection system: hardware and architectural outline of the Australian RRAMNS CTD system

NASA Astrophysics Data System (ADS)

Abeynayake, Canicious; Chant, Ian; Kempinger, Siegfried; Rye, Alan

2005-06-01

The Rapid Route Area and Mine Neutralisation System (RRAMNS) Capability Technology Demonstrator (CTD) is a countermine detection project undertaken by DSTO and supported by the Australian Defence Force (ADF). The limited time and budget for this CTD resulted in some difficult strategic decisions with regard to hardware selection and system architecture. Although the delivered system has certain limitations arising from its experimental status, many lessons have been learned which illustrate a pragmatic path for future development. RRAMNS a similar sensor suite to other systems, in that three complementary sensors are included. These are Ground Probing Radar, Metal Detector Array, and multi-band electro-optic sensors. However, RRAMNS uses a unique imaging system and a network based real-time control and sensor fusion architecture. The relatively simple integration of each of these components could be the basis for a robust and cost-effective operational system. The RRAMNS imaging system consists of three cameras which cover the visible spectrum, the mid-wave and long-wave infrared region. This subsystem can be used separately as a scouting sensor. This paper describes the system at its mid-2004 status, when full integration of all detection components was achieved.

Multi Sensor Fusion Framework for Indoor-Outdoor Localization of Limited Resource Mobile Robots

PubMed Central

Marín, Leonardo; Vallés, Marina; Soriano, Ángel; Valera, Ángel; Albertos, Pedro

2013-01-01

This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments. PMID:24152933

Motion-sensor fusion-based gesture recognition and its VLSI architecture design for mobile devices

NASA Astrophysics Data System (ADS)

Zhu, Wenping; Liu, Leibo; Yin, Shouyi; Hu, Siqi; Tang, Eugene Y.; Wei, Shaojun

2014-05-01

With the rapid proliferation of smartphones and tablets, various embedded sensors are incorporated into these platforms to enable multimodal human-computer interfaces. Gesture recognition, as an intuitive interaction approach, has been extensively explored in the mobile computing community. However, most gesture recognition implementations by now are all user-dependent and only rely on accelerometer. In order to achieve competitive accuracy, users are required to hold the devices in predefined manner during the operation. In this paper, a high-accuracy human gesture recognition system is proposed based on multiple motion sensor fusion. Furthermore, to reduce the energy overhead resulted from frequent sensor sampling and data processing, a high energy-efficient VLSI architecture implemented on a Xilinx Virtex-5 FPGA board is also proposed. Compared with the pure software implementation, approximately 45 times speed-up is achieved while operating at 20 MHz. The experiments show that the average accuracy for 10 gestures achieves 93.98% for user-independent case and 96.14% for user-dependent case when subjects hold the device randomly during completing the specified gestures. Although a few percent lower than the conventional best result, it still provides competitive accuracy acceptable for practical usage. Most importantly, the proposed system allows users to hold the device randomly during operating the predefined gestures, which substantially enhances the user experience.

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

PubMed

Deng, Xinyang; Jiang, Wen

2017-09-12

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

PubMed Central

Deng, Xinyang

2017-01-01

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model. PMID:28895905

Enhanced image capture through fusion

NASA Technical Reports Server (NTRS)

Burt, Peter J.; Hanna, Keith; Kolczynski, Raymond J.

1993-01-01

Image fusion may be used to combine images from different sensors, such as IR and visible cameras, to obtain a single composite with extended information content. Fusion may also be used to combine multiple images from a given sensor to form a composite image in which information of interest is enhanced. We present a general method for performing image fusion and show that this method is effective for diverse fusion applications. We suggest that fusion may provide a powerful tool for enhanced image capture with broad utility in image processing and computer vision.

Radar E-O image fusion

NASA Technical Reports Server (NTRS)

Oneil, William F.

1993-01-01

The fusion of radar and electro-optic (E-O) sensor images presents unique challenges. The two sensors measure different properties of the real three-dimensional (3-D) world. Forming the sensor outputs into a common format does not mask these differences. In this paper, the conditions under which fusion of the two sensor signals is possible are explored. The program currently planned to investigate this problem is briefly discussed.

[MRI/TRUS fusion-guided prostate biopsy : Value in the context of focal therapy].

PubMed

Franz, T; von Hardenberg, J; Blana, A; Cash, H; Baumunk, D; Salomon, G; Hadaschik, B; Henkel, T; Herrmann, J; Kahmann, F; Köhrmann, K-U; Köllermann, J; Kruck, S; Liehr, U-B; Machtens, S; Peters, I; Radtke, J P; Roosen, A; Schlemmer, H-P; Sentker, L; Wendler, J J; Witzsch, U; Stolzenburg, J-U; Schostak, M; Ganzer, R

2017-02-01

Several systems for MRI/TRUS fusion-guided biopsy of the prostate are commercially available. Many studies have shown superiority of fusion systems for tumor detection and diagnostic quality compared to random biopsy. The benefit of fusion systems in focal therapy of prostate cancer (PC) is less clear. Critical considerations of fusion systems for planning and monitoring of focal therapy of PC were investigated. A systematic literature review of available fusion systems for the period 2013-5/2016 was performed. A checklist of technical details, suitability for special anatomic situations and suitability for focal therapy was established by the German working group for focal therapy (Arbeitskreis fokale und Mikrotherapie). Eight fusion systems were considered (Artemis™, BioJet, BiopSee®, iSR´obot™ Mona Lisa, Hitachi HI-RVS, UroNav and Urostation®). Differences were found for biopsy mode (transrectal, perineal, both), fusion mode (elastic or rigid), navigation (image-based, electromagnetic sensor-based or mechanical sensor-based) and space requirements. Several consensus groups recommend fusion systems for focal therapy. Useful features are "needle tracking" and compatibility between fusion system and treatment device (available for Artemis™, BiopSee® and Urostation® with Focal One®; BiopSee®, Hitachi HI-RVS with NanoKnife®; BioJet, BiopSee® with cryoablation, brachytherapy). There are a few studies for treatment planning. However, studies on treatment monitoring after focal therapy are missing.

A real-time photogrammetric algorithm for sensor and synthetic image fusion with application to aviation combined vision

NASA Astrophysics Data System (ADS)

Lebedev, M. A.; Stepaniants, D. G.; Komarov, D. V.; Vygolov, O. V.; Vizilter, Yu. V.; Zheltov, S. Yu.

2014-08-01

The paper addresses a promising visualization concept related to combination of sensor and synthetic images in order to enhance situation awareness of a pilot during an aircraft landing. A real-time algorithm for a fusion of a sensor image, acquired by an onboard camera, and a synthetic 3D image of the external view, generated in an onboard computer, is proposed. The pixel correspondence between the sensor and the synthetic images is obtained by an exterior orientation of a "virtual" camera using runway points as a geospatial reference. The runway points are detected by the Projective Hough Transform, which idea is to project the edge map onto a horizontal plane in the object space (the runway plane) and then to calculate intensity projections of edge pixels on different directions of intensity gradient. The performed experiments on simulated images show that on a base glide path the algorithm provides image fusion with pixel accuracy, even in the case of significant navigation errors.

Metal Hall sensors for the new generation fusion reactors of DEMO scale

NASA Astrophysics Data System (ADS)

Bolshakova, I.; Bulavin, M.; Kargin, N.; Kost, Ya.; Kuech, T.; Kulikov, S.; Radishevskiy, M.; Shurygin, F.; Strikhanov, M.; Vasil'evskii, I.; Vasyliev, A.

2017-11-01

For the first time, the results of on-line testing of metal Hall sensors based on nano-thickness (50-70) nm gold films, which was conducted under irradiation by high-energy neutrons up to the high fluences of 1 · 1024 n · m-2, are presented. The testing has been carried out in the IBR-2 fast pulsed reactor in the neutron flux with the intensity of 1.5 · 1017 n · m-2 · s-1 at the Joint Institute for Nuclear Research. The energy spectrum of neutron flux was very close to that expected for the ex-vessel sensors locations in the ITER experimental reactor. The magnetic field sensitivity of the gold sensors was stable within the whole fluence range under research. Also, sensitivity values at the start and at the end of irradiation session were equal within the measurement error (<1%). The results obtained make it possible to recommend gold sensors for magnetic diagnostics in the new generation fusion reactors of DEMO scale.

Pulse-coupled neural network sensor fusion

NASA Astrophysics Data System (ADS)

Johnson, John L.; Schamschula, Marius P.; Inguva, Ramarao; Caulfield, H. John

1998-03-01

Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex--the PCNN or Pulse Coupled Neural Network--performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the 3D PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting 2D (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter- term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single 2D pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.

Sensor validation and fusion for gas turbine vibration monitoring

NASA Astrophysics Data System (ADS)

Yan, Weizhong; Goebel, Kai F.

2003-08-01

Vibration monitoring is an important practice throughout regular operation of gas turbine power systems and, even more so, during characterization tests. Vibration monitoring relies on accurate and reliable sensor readings. To obtain accurate readings, sensors are placed such that the signal is maximized. In the case of characterization tests, strain gauges are placed at the location of vibration modes on blades inside the gas turbine. Due to the prevailing harsh environment, these sensors have a limited life and decaying accuracy, both of which impair vibration assessment. At the same time bandwidth limitations may restrict data transmission, which in turn limits the number of sensors that can be used for assessment. Knowing the sensor status (normal or faulty), and more importantly, knowing the true vibration level of the system all the time is essential for successful gas turbine vibration monitoring. This paper investigates a dynamic sensor validation and system health reasoning scheme that addresses the issues outlined above by considering only the information required to reliably assess system health status. In particular, if abnormal system health is suspected or if the primary sensor is determined to be faulted, information from available "sibling" sensors is dynamically integrated. A confidence expresses the complex interactions of sensor health and system health, their reliabilities, conflicting information, and what the health assessment is. Effectiveness of the scheme in achieving accurate and reliable vibration evaluation is then demonstrated using a combination of simulated data and a small sample of a real-world application data where the vibration of compressor blades during a real time characterization test of a new gas turbine power system is monitored.

Semiotic foundation for multisensor-multilook fusion

NASA Astrophysics Data System (ADS)

Myler, Harley R.

1998-07-01

This paper explores the concept of an application of semiotic principles to the design of a multisensor-multilook fusion system. Semiotics is an approach to analysis that attempts to process media in a united way using qualitative methods as opposed to quantitative. The term semiotic refers to signs, or signatory data that encapsulates information. Semiotic analysis involves the extraction of signs from information sources and the subsequent processing of the signs into meaningful interpretations of the information content of the source. The multisensor fusion problem predicated on a semiotic system structure and incorporating semiotic analysis techniques is explored and the design for a multisensor system as an information fusion system is explored. Semiotic analysis opens the possibility of using non-traditional sensor sources and modalities in the fusion process, such as verbal and textual intelligence derived from human observers. Examples of how multisensor/multimodality data might be analyzed semiotically is shown and discussion on how a semiotic system for multisensor fusion could be realized is outlined. The architecture of a semiotic multisensor fusion processor that can accept situational awareness data is described, although an implementation has not as yet been constructed.

HALO: a reconfigurable image enhancement and multisensor fusion system

NASA Astrophysics Data System (ADS)

Wu, F.; Hickman, D. L.; Parker, Steve J.

2014-06-01

Contemporary high definition (HD) cameras and affordable infrared (IR) imagers are set to dramatically improve the effectiveness of security, surveillance and military vision systems. However, the quality of imagery is often compromised by camera shake, or poor scene visibility due to inadequate illumination or bad atmospheric conditions. A versatile vision processing system called HALO™ is presented that can address these issues, by providing flexible image processing functionality on a low size, weight and power (SWaP) platform. Example processing functions include video distortion correction, stabilisation, multi-sensor fusion and image contrast enhancement (ICE). The system is based around an all-programmable system-on-a-chip (SoC), which combines the computational power of a field-programmable gate array (FPGA) with the flexibility of a CPU. The FPGA accelerates computationally intensive real-time processes, whereas the CPU provides management and decision making functions that can automatically reconfigure the platform based on user input and scene content. These capabilities enable a HALO™ equipped reconnaissance or surveillance system to operate in poor visibility, providing potentially critical operational advantages in visually complex and challenging usage scenarios. The choice of an FPGA based SoC is discussed, and the HALO™ architecture and its implementation are described. The capabilities of image distortion correction, stabilisation, fusion and ICE are illustrated using laboratory and trials data.

Scalable sensor management for automated fusion and tactical reconnaissance

NASA Astrophysics Data System (ADS)

Walls, Thomas J.; Wilson, Michael L.; Partridge, Darin C.; Haws, Jonathan R.; Jensen, Mark D.; Johnson, Troy R.; Petersen, Brad D.; Sullivan, Stephanie W.

2013-05-01

The capabilities of tactical intelligence, surveillance, and reconnaissance (ISR) payloads are expanding from single sensor imagers to integrated systems-of-systems architectures. Increasingly, these systems-of-systems include multiple sensing modalities that can act as force multipliers for the intelligence analyst. Currently, the separate sensing modalities operate largely independent of one another, providing a selection of operating modes but not an integrated intelligence product. We describe here a Sensor Management System (SMS) designed to provide a small, compact processing unit capable of managing multiple collaborative sensor systems on-board an aircraft. Its purpose is to increase sensor cooperation and collaboration to achieve intelligent data collection and exploitation. The SMS architecture is designed to be largely sensor and data agnostic and provide flexible networked access for both data providers and data consumers. It supports pre-planned and ad-hoc missions, with provisions for on-demand tasking and updates from users connected via data links. Management of sensors and user agents takes place over standard network protocols such that any number and combination of sensors and user agents, either on the local network or connected via data link, can register with the SMS at any time during the mission. The SMS provides control over sensor data collection to handle logging and routing of data products to subscribing user agents. It also supports the addition of algorithmic data processing agents for feature/target extraction and provides for subsequent cueing from one sensor to another. The SMS architecture was designed to scale from a small UAV carrying a limited number of payloads to an aircraft carrying a large number of payloads. The SMS system is STANAG 4575 compliant as a removable memory module (RMM) and can act as a vehicle specific module (VSM) to provide STANAG 4586 compliance (level-3 interoperability) to a non-compliant sensor system

New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2017-07-01

This paper studies the distributed fusion estimation problem from multisensor measured outputs perturbed by correlated noises and uncertainties modelled by random parameter matrices. Each sensor transmits its outputs to a local processor over a packet-erasure channel and, consequently, random losses may occur during transmission. Different white sequences of Bernoulli variables are introduced to model the transmission losses. For the estimation, each lost output is replaced by its estimator based on the information received previously, and only the covariances of the processes involved are used, without requiring the signal evolution model. First, a recursive algorithm for the local least-squares filters is derived by using an innovation approach. Then, the cross-correlation matrices between any two local filters is obtained. Finally, the distributed fusion filter weighted by matrices is obtained from the local filters by applying the least-squares criterion. The performance of the estimators and the influence of both sensor uncertainties and transmission losses on the estimation accuracy are analysed in a numerical example.

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

NASA Technical Reports Server (NTRS)

Leptoukh, Gregory; Zubko, V.; Gopalan, A.

2007-01-01

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion.

PubMed

Ljungblad, Jonas; Hök, Bertil; Allalou, Amin; Pettersson, Håkan

2017-05-29

The research objective of the present investigation is to demonstrate the present status of passive in-vehicle driver breath alcohol detection and highlight the necessary conditions for large-scale implementation of such a system. Completely passive detection has remained a challenge mainly because of the requirements on signal resolution combined with the constraints of vehicle integration. The work is part of the Driver Alcohol Detection System for Safety (DADSS) program aiming at massive deployment of alcohol sensing systems that could potentially save thousands of American lives annually. The work reported here builds on earlier investigations, in which it has been shown that detection of alcohol vapor in the proximity of a human subject may be traced to that subject by means of simultaneous recording of carbon dioxide (CO 2 ) at the same location. Sensors based on infrared spectroscopy were developed to detect and quantify low concentrations of alcohol and CO 2 . In the present investigation, alcohol and CO 2 were recorded at various locations in a vehicle cabin while human subjects were performing normal in-step procedures and driving preparations. A video camera directed to the driver position was recording images of the driver's upper body parts, including the face, and the images were analyzed with respect to features of significance to the breathing behavior and breath detection, such as mouth opening and head direction. Improvement of the sensor system with respect to signal resolution including algorithm and software development, and fusion of the sensor and camera signals was successfully implemented and tested before starting the human study. In addition, experimental tests and simulations were performed with the purpose of connecting human subject data with repeatable experimental conditions. The results include occurrence statistics of detected breaths by signal peaks of CO 2 and alcohol. From the statistical data, the accuracy of breath alcohol

An Autonomous Sensor System Architecture for Active Flow and Noise Control Feedback

NASA Technical Reports Server (NTRS)

Humphreys, William M, Jr.; Culliton, William G.

2008-01-01

Multi-channel sensor fusion represents a powerful technique to simply and efficiently extract information from complex phenomena. While the technique has traditionally been used for military target tracking and situational awareness, a study has been successfully completed that demonstrates that sensor fusion can be applied equally well to aerodynamic applications. A prototype autonomous hardware processor was successfully designed and used to detect in real-time the two-dimensional flow reattachment location generated by a simple separated-flow wind tunnel model. The success of this demonstration illustrates the feasibility of using autonomous sensor processing architectures to enhance flow control feedback signal generation.

A Novel Sensor System for Measuring Wheel Loads of Vehicles on Highways

PubMed Central

Zhang, Wenbin; Suo, Chunguang; Wang, Qi

2008-01-01

With the development of the highway transportation and business trade, vehicle Weigh-In-Motion (WIM) technology has become a key technology for measuring traffic loads. In this paper a novel WIM system based on monitoring of pavement strain responses in rigid pavement was investigated. In this WIM system multiple low cost, light weight, small volume and high accuracy embedded concrete strain sensors were used as WIM sensors to measure rigid pavement strain responses. In order to verify the feasibility of the method, a system prototype based on multiple sensors was designed and deployed on a relatively busy freeway. Field calibration and tests were performed with known two-axle truck wheel loads and the measurement errors were calculated based on the static weights measured with a static weighbridge. This enables the weights of other vehicles to be calculated from the calibration constant. Calibration and test results for individual sensors or three-sensor fusions are both provided. Repeatability, sources of error, and weight accuracy are discussed. Successful results showed that the proposed method was feasible and proven to have a high accuracy. Furthermore, a sample mean approach using multiple fused individual sensors could provide better performance compared to individual sensors. PMID:27873952

Autonomous sensor manager agents (ASMA)

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.

2004-04-01

Autonomous sensor manager agents are presented as an algorithm to perform sensor management within a multisensor fusion network. The design of the hybrid ant system/particle swarm agents is described in detail with some insight into their performance. Although the algorithm is designed for the general sensor management problem, a simulation example involving 2 radar systems is presented. Algorithmic parameters are determined by the size of the region covered by the sensor network, the number of sensors, and the number of parameters to be selected. With straight forward modifications, this algorithm can be adapted for most sensor management problems.

A System to Provide Real-Time Collaborative Situational Awareness by Web Enabling a Distributed Sensor Network

NASA Technical Reports Server (NTRS)

Panangadan, Anand; Monacos, Steve; Burleigh, Scott; Joswig, Joseph; James, Mark; Chow, Edward

2012-01-01

In this paper, we describe the architecture of both the PATS and SAP systems and how these two systems interoperate with each other forming a unified capability for deploying intelligence in hostile environments with the objective of providing actionable situational awareness of individuals. The SAP system works in concert with the UICDS information sharing middleware to provide data fusion from multiple sources. UICDS can then publish the sensor data using the OGC's Web Mapping Service, Web Feature Service, and Sensor Observation Service standards. The system described in the paper is able to integrate a spatially distributed sensor system, operating without the benefit of the Web infrastructure, with a remote monitoring and control system that is equipped to take advantage of SWE.

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion

NASA Astrophysics Data System (ADS)

Kwon, Seong Kyung; Hyun, Eugin; Lee, Jin-Hee; Lee, Jonghun; Son, Sang Hyuk

2017-11-01

Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar-radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

Effect of cross-correlation on track-to-track fusion

NASA Astrophysics Data System (ADS)

Saha, Rajat K.

1994-07-01

Since the advent of target tracking systems employing a diverse mixture of sensors, there has been increasing recognition by air defense system planners and other military system analysts of the need to integrate these tracks so that a clear air picture can be obtained in a command center. A popular methodology to achieve this goal is to perform track-to-track fusion, which performs track-to-track association as well as kinematic state vector fusion. This paper seeks to answer analytically the extent of improvement achievable by means of kinetic state vector fusion when the tracks are obtained from dissimilar sensors (e.g., Radar/ESM/IRST/IFF). It is well known that evaluation of the performance of state vector fusion algorithms at steady state must take into account the effects of cross-correlation between eligible tracks introduced by the input noise which, unfortunately, is often neglected because of added computational complexity. In this paper, an expression for the steady-state cross-covariance matrix for a 2D state vector track-to-track fusion is obtained. This matrix is shown to be a function of the parameters of the Kalman filters associated with the candidate tracks being fused. Conditions for positive definiteness of the cross-covariance matrix have been derived and the effect of positive definiteness on performance of track-to-track fusion is also discussed.

Dynamic image fusion and general observer preference

NASA Astrophysics Data System (ADS)

Burks, Stephen D.; Doe, Joshua M.

2010-04-01

Recent developments in image fusion give the user community many options for ways of presenting the imagery to an end-user. Individuals at the US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate have developed an electronic system that allows users to quickly and efficiently determine optimal image fusion algorithms and color parameters based upon collected imagery and videos from environments that are typical to observers in a military environment. After performing multiple multi-band data collections in a variety of military-like scenarios, different waveband, fusion algorithm, image post-processing, and color choices are presented to observers as an output of the fusion system. The observer preferences can give guidelines as to how specific scenarios should affect the presentation of fused imagery.

Engineering workstation: Sensor modeling

NASA Technical Reports Server (NTRS)

Pavel, M; Sweet, B.

1993-01-01

The purpose of the engineering workstation is to provide an environment for rapid prototyping and evaluation of fusion and image processing algorithms. Ideally, the algorithms are designed to optimize the extraction of information that is useful to a pilot for all phases of flight operations. Successful design of effective fusion algorithms depends on the ability to characterize both the information available from the sensors and the information useful to a pilot. The workstation is comprised of subsystems for simulation of sensor-generated images, image processing, image enhancement, and fusion algorithms. As such, the workstation can be used to implement and evaluate both short-term solutions and long-term solutions. The short-term solutions are being developed to enhance a pilot's situational awareness by providing information in addition to his direct vision. The long term solutions are aimed at the development of complete synthetic vision systems. One of the important functions of the engineering workstation is to simulate the images that would be generated by the sensors. The simulation system is designed to use the graphics modeling and rendering capabilities of various workstations manufactured by Silicon Graphics Inc. The workstation simulates various aspects of the sensor-generated images arising from phenomenology of the sensors. In addition, the workstation can be used to simulate a variety of impairments due to mechanical limitations of the sensor placement and due to the motion of the airplane. Although the simulation is currently not performed in real-time, sequences of individual frames can be processed, stored, and recorded in a video format. In that way, it is possible to examine the appearance of different dynamic sensor-generated and fused images.

Adaptive Multi-sensor Data Fusion Model for In-situ Exploration of Mars

NASA Astrophysics Data System (ADS)

Schneiderman, T.; Sobron, P.

2014-12-01

Laser Raman spectroscopy (LRS) and laser-induced breakdown spectroscopy (LIBS) can be used synergistically to characterize the geochemistry and mineralogy of potential microbial habitats and biosignatures. The value of LRS and LIBS has been recognized by the planetary science community: (i) NASA's Mars2020 mission features a combined LRS-LIBS instrument, SuperCam, and an LRS instrument, SHERLOC; (ii) an LRS instrument, RLS, will fly on ESA's 2018 ExoMars mission. The advantages of combining LRS and LIBS are evident: (1) LRS/LIBS can share hardware components; (2) LIBS reveals the relative concentration of major (and often trace) elements present in a sample; and (3) LRS yields information on the individual mineral species and their chemical/structural nature. Combining data from LRS and LIBS enables definitive mineral phase identification with precise chemical characterization of major, minor, and trace mineral species. New approaches to data processing are needed to analyze large amounts of LRS+LIBS data efficiently and maximize the scientific return of integrated measurements. Multi-sensor data fusion (MSDF) is a method that allows for robust sample identification through automated acquisition, processing, and combination of data. It optimizes information usage, yielding a more robust characterization of a target than could be acquired through single sensor use. We have developed a prototype fuzzy logic adaptive MSDF model aimed towards the unsupervised characterization of Martian habitats and their biosignatures using LRS and LIBS datasets. Our model also incorporates fusion of microimaging (MI) data - critical for placing analyses in geological and spatial context. Here, we discuss the performance of our novel MSDF model and demonstrate that automated quantification of the salt abundance in sulfate/clay/phyllosilicate mixtures is possible through data fusion of collocated LRS, LIBS, and MI data.

Laser erosion diagnostics of plasma facing materials with displacement sensors and their application to safeguard monitors to protect nuclear fusion chambers

NASA Astrophysics Data System (ADS)

Kasuya, Koichi; Motokoshi, Shinji; Taniguchi, Seiji; Nakai, Mitsuo; Tokunaga, Kazutoshi; Mroz, Waldemar; Budner, Boguslaw; Korczyc, Barbara

2015-02-01

Tungsten and SiC are candidates for the structural materials of the nuclear fusion reactor walls, while CVD poly-crystal diamond is candidate for the window material under the hazardous fusion stresses. We measured the surface endurance strength of such materials with commercial displacement sensors and our recent evaluation method. The pulsed high thermal input was put into the material surfaces by UV lasers, and the surface erosions were diagnosed. With the increase of the total number of the laser shots per position, the crater depth increased gradually. The 3D and 2D pictures of the craters were gathered and compared under various experimental conditions. For example, the maximum crater depths were plotted as a function of shot accumulated numbers, from which we evaluated the threshold thermal input for the surface erosions to be induced. The simple comparison-result showed that tungsten was stronger roughly two times than SiC. Then we proposed how to monitor the surface conditions of combined samples with such diamonds coated with thin tungsten layers, when we use such samples as parts of divertor inner walls, fusion chamber first walls, and various diagnostic windows. We investigated how we might be able to measure the inner surface erosions with the same kinds of displacement sensors. We found out the measurable maximum thickness of such diamond which is useful to monitor the erosion. Additionally we showed a new scheme of fusion reactor systems with injectors for anisotropic pellets and heating lasers under the probable use of W and/or SiC.

Fusion of Laser Altimetry Data with Dems Derived from Stereo Imaging Systems

NASA Astrophysics Data System (ADS)

Schenk, T.; Csatho, B. M.; Duncan, K.

2016-06-01

During the last two decades surface elevation data have been gathered over the Greenland Ice Sheet (GrIS) from a variety of different sensors including spaceborne and airborne laser altimetry, such as NASA's Ice Cloud and land Elevation Satellite (ICESat), Airborne Topographic Mapper (ATM) and Laser Vegetation Imaging Sensor (LVIS), as well as from stereo satellite imaging systems, most notably from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Worldview. The spatio-temporal resolution, the accuracy, and the spatial coverage of all these data differ widely. For example, laser altimetry systems are much more accurate than DEMs derived by correlation from imaging systems. On the other hand, DEMs usually have a superior spatial resolution and extended spatial coverage. We present in this paper an overview of the SERAC (Surface Elevation Reconstruction And Change detection) system, designed to cope with the data complexity and the computation of elevation change histories. SERAC simultaneously determines the ice sheet surface shape and the time-series of elevation changes for surface patches whose size depends on the ruggedness of the surface and the point distribution of the sensors involved. By incorporating different sensors, SERAC is a true fusion system that generates the best plausible result (time series of elevation changes) a result that is better than the sum of its individual parts. We follow this up with an example of the Helmheim gacier, involving ICESat, ATM and LVIS laser altimetry data, together with ASTER DEMs.

Concept of electro-optical sensor module for sniper detection system

NASA Astrophysics Data System (ADS)

Trzaskawka, Piotr; Dulski, Rafal; Kastek, Mariusz

2010-10-01

The paper presents an initial concept of the electro-optical sensor unit for sniper detection purposes. This unit, comprising of thermal and daylight cameras, can operate as a standalone device but its primary application is a multi-sensor sniper and shot detection system. Being a part of a larger system it should contribute to greater overall system efficiency and lower false alarm rate thanks to data and sensor fusion techniques. Additionally, it is expected to provide some pre-shot detection capabilities. Generally acoustic (or radar) systems used for shot detection offer only "after-the-shot" information and they cannot prevent enemy attack, which in case of a skilled sniper opponent usually means trouble. The passive imaging sensors presented in this paper, together with active systems detecting pointed optics, are capable of detecting specific shooter signatures or at least the presence of suspected objects in the vicinity. The proposed sensor unit use thermal camera as a primary sniper and shot detection tool. The basic camera parameters such as focal plane array size and type, focal length and aperture were chosen on the basis of assumed tactical characteristics of the system (mainly detection range) and current technology level. In order to provide costeffective solution the commercially available daylight camera modules and infrared focal plane arrays were tested, including fast cooled infrared array modules capable of 1000 fps image acquisition rate. The daylight camera operates as a support, providing corresponding visual image, easier to comprehend for a human operator. The initial assumptions concerning sensor operation were verified during laboratory and field test and some example shot recording sequences are presented.

Benchmarking image fusion system design parameters

NASA Astrophysics Data System (ADS)

Howell, Christopher L.

2013-06-01

A clear and absolute method for discriminating between image fusion algorithm performances is presented. This method can effectively be used to assist in the design and modeling of image fusion systems. Specifically, it is postulated that quantifying human task performance using image fusion should be benchmarked to whether the fusion algorithm, at a minimum, retained the performance benefit achievable by each independent spectral band being fused. The established benchmark would then clearly represent the threshold that a fusion system should surpass to be considered beneficial to a particular task. A genetic algorithm is employed to characterize the fused system parameters using a Matlab® implementation of NVThermIP as the objective function. By setting the problem up as a mixed-integer constraint optimization problem, one can effectively look backwards through the image acquisition process: optimizing fused system parameters by minimizing the difference between modeled task difficulty measure and the benchmark task difficulty measure. The results of an identification perception experiment are presented, where human observers were asked to identify a standard set of military targets, and used to demonstrate the effectiveness of the benchmarking process.

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems.

PubMed

Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

2015-07-23

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other.

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems

PubMed Central

Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

2015-01-01

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other. PMID:26213932

Attack Detection in Sensor Network Target Localization Systems With Quantized Data

NASA Astrophysics Data System (ADS)

Zhang, Jiangfan; Wang, Xiaodong; Blum, Rick S.; Kaplan, Lance M.

2018-04-01

We consider a sensor network focused on target localization, where sensors measure the signal strength emitted from the target. Each measurement is quantized to one bit and sent to the fusion center. A general attack is considered at some sensors that attempts to cause the fusion center to produce an inaccurate estimation of the target location with a large mean-square-error. The attack is a combination of man-in-the-middle, hacking, and spoofing attacks that can effectively change both signals going into and coming out of the sensor nodes in a realistic manner. We show that the essential effect of attacks is to alter the estimated distance between the target and each attacked sensor to a different extent, giving rise to a geometric inconsistency among the attacked and unattacked sensors. Hence, with the help of two secure sensors, a class of detectors are proposed to detect the attacked sensors by scrutinizing the existence of the geometric inconsistency. We show that the false alarm and miss probabilities of the proposed detectors decrease exponentially as the number of measurement samples increases, which implies that for sufficiently large number of samples, the proposed detectors can identify the attacked and unattacked sensors with any required accuracy.

Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

PubMed

Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

2011-01-01

In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.

Fusion of magnetometer and gradiometer sensors of MEG in the presence of multiplicative error.

PubMed

Mohseni, Hamid R; Woolrich, Mark W; Kringelbach, Morten L; Luckhoo, Henry; Smith, Penny Probert; Aziz, Tipu Z

2012-07-01

Novel neuroimaging techniques have provided unprecedented information on the structure and function of the living human brain. Multimodal fusion of data from different sensors promises to radically improve this understanding, yet optimal methods have not been developed. Here, we demonstrate a novel method for combining multichannel signals. We show how this method can be used to fuse signals from the magnetometer and gradiometer sensors used in magnetoencephalography (MEG), and through extensive experiments using simulation, head phantom and real MEG data, show that it is both robust and accurate. This new approach works by assuming that the lead fields have multiplicative error. The criterion to estimate the error is given within a spatial filter framework such that the estimated power is minimized in the worst case scenario. The method is compared to, and found better than, existing approaches. The closed-form solution and the conditions under which the multiplicative error can be optimally estimated are provided. This novel approach can also be employed for multimodal fusion of other multichannel signals such as MEG and EEG. Although the multiplicative error is estimated based on beamforming, other methods for source analysis can equally be used after the lead-field modification.

Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

2015-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, MODIS, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. HySDS is a Hybrid-Cloud Science Data System that has been developed and applied under NASA AIST, MEaSUREs, and ACCESS grants. HySDS uses the SciFlow workflow engine to partition analysis workflows into parallel tasks (e.g. segmenting by time or space) that are pushed into a durable job queue. The tasks are "pulled" from the queue by worker Virtual Machines (VM's) and executed in an on-premise Cloud (Eucalyptus or OpenStack) or at Amazon in the public Cloud or govCloud. In this way, years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the transferred data. We are using HySDS to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a MEASURES grant. We will present the architecture of HySDS, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. Our system demonstrates how one can pull A-Train variables (Levels 2 & 3) on-demand into the Amazon Cloud, and cache only those variables that are heavily used, so that any number of compute jobs can be

A Novel Remote Rehabilitation System with the Fusion of Noninvasive Wearable Device and Motion Sensing for Pulmonary Patients.

PubMed

Tey, Chuang-Kit; An, Jinyoung; Chung, Wan-Young

2017-01-01

Chronic obstructive pulmonary disease is a type of lung disease caused by chronically poor airflow that makes breathing difficult. As a chronic illness, it typically worsens over time. Therefore, pulmonary rehabilitation exercises and patient management for extensive periods of time are required. This paper presents a remote rehabilitation system for a multimodal sensors-based application for patients who have chronic breathing difficulties. The process involves the fusion of sensory data-captured motion data by stereo-camera and photoplethysmogram signal by a wearable PPG sensor-that are the input variables of a detection and evaluation framework. In addition, we incorporated a set of rehabilitation exercises specific for pulmonary patients into the system by fusing sensory data. Simultaneously, the system also features medical functions that accommodate the needs of medical professionals and those which ease the use of the application for patients, including exercises for tracking progress, patient performance, exercise assignments, and exercise guidance. Finally, the results indicate the accurate determination of pulmonary exercises from the fusion of sensory data. This remote rehabilitation system provides a comfortable and cost-effective option in the healthcare rehabilitation system.

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

PubMed Central

Peralta, Emmanuel; Vargas, Héctor; Hermosilla, Gabriel

2018-01-01

Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks. PMID:29495338

Naval sensor data database (NSDD)

NASA Astrophysics Data System (ADS)

Robertson, Candace J.; Tubridy, Lisa H.

1999-08-01

The Naval Sensor Data database (NSDD) is a multi-year effort to archive, catalogue, and disseminate data from all types of sensors to the mine warfare, signal and image processing, and sensor development communities. The purpose is to improve and accelerate research and technology. Providing performers with the data required to develop and validate improvements in hardware, simulation, and processing will foster advances in sensor and system performance. The NSDD will provide a centralized source of sensor data in its associated ground truth, which will support an improved understanding will be benefited in the areas of signal processing, computer-aided detection and classification, data compression, data fusion, and geo-referencing, as well as sensor and sensor system design.

Sensor feature fusion for detecting buried objects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-04-01

Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less

Reovirus FAST Proteins Drive Pore Formation and Syncytiogenesis Using a Novel Helix-Loop-Helix Fusion-Inducing Lipid Packing Sensor

PubMed Central

Sarker, Muzaddid; de Antueno, Roberto; Langelaan, David N.; Parmar, Hiren B.; Shin, Kyungsoo; Rainey, Jan K.; Duncan, Roy

2015-01-01

Pore formation is the most energy-demanding step during virus-induced membrane fusion, where high curvature of the fusion pore rim increases the spacing between lipid headgroups, exposing the hydrophobic interior of the membrane to water. How protein fusogens breach this thermodynamic barrier to pore formation is unclear. We identified a novel fusion-inducing lipid packing sensor (FLiPS) in the cytosolic endodomain of the baboon reovirus p15 fusion-associated small transmembrane (FAST) protein that is essential for pore formation during cell-cell fusion and syncytiogenesis. NMR spectroscopy and mutational studies indicate the dependence of this FLiPS on a hydrophobic helix-loop-helix structure. Biochemical and biophysical assays reveal the p15 FLiPS preferentially partitions into membranes with high positive curvature, and this partitioning is impeded by bis-ANS, a small molecule that inserts into hydrophobic defects in membranes. Most notably, the p15 FLiPS can be functionally replaced by heterologous amphipathic lipid packing sensors (ALPS) but not by other membrane-interactive amphipathic helices. Furthermore, a previously unrecognized amphipathic helix in the cytosolic domain of the reptilian reovirus p14 FAST protein can functionally replace the p15 FLiPS, and is itself replaceable by a heterologous ALPS motif. Anchored near the cytoplasmic leaflet by the FAST protein transmembrane domain, the FLiPS is perfectly positioned to insert into hydrophobic defects that begin to appear in the highly curved rim of nascent fusion pores, thereby lowering the energy barrier to stable pore formation. PMID:26061049

Statistical Sensor Fusion of a 9-DOF Mems Imu for Indoor Navigation

NASA Astrophysics Data System (ADS)

Chow, J. C. K.

2017-09-01

Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8-89.5 % and the dead-reckoned positioning accuracy by 72.9-92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.

A weighted optimization approach to time-of-flight sensor fusion.

PubMed

Schwarz, Sebastian; Sjostrom, Marten; Olsson, Roger

2014-01-01

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

Projection-based circular constrained state estimation and fusion over long-haul links

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Qiang; Rao, Nageswara S.

In this paper, we consider a scenario where sensors are deployed over a large geographical area for tracking a target with circular nonlinear constraints on its motion dynamics. The sensor state estimates are sent over long-haul networks to a remote fusion center for fusion. We are interested in different ways to incorporate the constraints into the estimation and fusion process in the presence of communication loss. In particular, we consider closed-form projection-based solutions, including rules for fusing the estimates and for incorporating the constraints, which jointly can guarantee timely fusion often required in realtime systems. We test the performance ofmore » these methods in the long-haul tracking environment using a simple example.« less

Improved detection probability of low level light and infrared image fusion system

NASA Astrophysics Data System (ADS)

Luo, Yuxiang; Fu, Rongguo; Zhang, Junju; Wang, Wencong; Chang, Benkang

2018-02-01

Low level light(LLL) image contains rich information on environment details, but is easily affected by the weather. In the case of smoke, rain, cloud or fog, much target information will lose. Infrared image, which is from the radiation produced by the object itself, can be "active" to obtain the target information in the scene. However, the image contrast and resolution is bad, the ability of the acquisition of target details is very poor, and the imaging mode does not conform to the human visual habit. The fusion of LLL and infrared image can make up for the deficiency of each sensor and give play to the advantages of single sensor. At first, we show the hardware design of fusion circuit. Then, through the recognition probability calculation of the target(one person) and the background image(trees), we find that the trees detection probability of LLL image is higher than that of the infrared image, and the person detection probability of the infrared image is obviously higher than that of LLL image. The detection probability of fusion image for one person and trees is higher than that of single detector. Therefore, image fusion can significantly enlarge recognition probability and improve detection efficiency.

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field

PubMed Central

Castrignanò, Annamaria; Quarto, Ruggiero; Vitti, Carolina; Langella, Giuliano; Terribile, Fabio

2017-01-01

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed. PMID:29207510

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field.

PubMed

Castrignanò, Annamaria; Buttafuoco, Gabriele; Quarto, Ruggiero; Vitti, Carolina; Langella, Giuliano; Terribile, Fabio; Venezia, Accursio

2017-12-03

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0-1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

Reconnaissance blind multi-chess: an experimentation platform for ISR sensor fusion and resource management

NASA Astrophysics Data System (ADS)

Newman, Andrew J.; Richardson, Casey L.; Kain, Sean M.; Stankiewicz, Paul G.; Guseman, Paul R.; Schreurs, Blake A.; Dunne, Jeffrey A.

2016-05-01

This paper introduces the game of reconnaissance blind multi-chess (RBMC) as a paradigm and test bed for understanding and experimenting with autonomous decision making under uncertainty and in particular managing a network of heterogeneous Intelligence, Surveillance and Reconnaissance (ISR) sensors to maintain situational awareness informing tactical and strategic decision making. The intent is for RBMC to serve as a common reference or challenge problem in fusion and resource management of heterogeneous sensor ensembles across diverse mission areas. We have defined a basic rule set and a framework for creating more complex versions, developed a web-based software realization to serve as an experimentation platform, and developed some initial machine intelligence approaches to playing it.

Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots

NASA Astrophysics Data System (ADS)

Emter, Thomas; Petereit, Janko

2014-05-01

An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.

Revisions to the JDL data fusion model

NASA Astrophysics Data System (ADS)

Steinberg, Alan N.; Bowman, Christopher L.; White, Franklin E.

1999-03-01

The Data Fusion Model maintained by the Joint Directors of Laboratories (JDL) Data Fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise the expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi- sensor/multi-source systems. Data fusion involves combining information - in the broadest sense - to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people, it can be useful to include consideration of informational and perceptual states in addition to the physical state. Developing cost-effective multi-source information systems requires a method for specifying data fusion processing and control functions, interfaces, and associate databases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology: current developments do not lend themselves to objective evaluation, comparison or re-use. This paper reports on proposed revisions and expansions of the JDL Data FUsion model to remedy some of these deficiencies. This involves broadening the functional model and related taxonomy beyond the original military focus, and integrating the Data Fusion Tree Architecture model for system description, design and development.

Track classification within wireless sensor network

NASA Astrophysics Data System (ADS)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Tarighati, Alla; Gross, James; Jalden, Joakim

2017-09-01

We consider the problem of decentralized hypothesis testing in a network of energy harvesting sensors, where sensors make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center. The fusion center makes a decision about the present hypothesis using the aggregate received data during a time interval. We explicitly consider a scenario under which the messages are sent through parallel access channels towards the fusion center. To avoid limited lifetime issues, we assume each sensor is capable of harvesting all the energy it needs for the communication from the environment. Each sensor has an energy buffer (battery) to save its harvested energy for use in other time intervals. Our key contribution is to formulate the problem of decentralized detection in a sensor network with energy harvesting devices. Our analysis is based on a queuing-theoretic model for the battery and we propose a sensor decision design method by considering long term energy management at the sensors. We show how the performance of the system changes for different battery capacities. We then numerically show how our findings can be used in the design of sensor networks with energy harvesting sensors.

Fusion of radar and satellite target measurements

NASA Astrophysics Data System (ADS)

Moy, Gabriel; Blaty, Donald; Farber, Morton; Nealy, Carlton

2011-06-01

A potentially high payoff for the ballistic missile defense system (BMDS) is the ability to fuse the information gathered by various sensor systems. In particular, it may be valuable in the future to fuse measurements made using ground based radars with passive measurements obtained from satellite-based EO/IR sensors. This task can be challenging in a multitarget environment in view of the widely differing resolution between active ground-based radar and an observation made by a sensor at long range from a satellite platform. Additionally, each sensor system could have a residual pointing bias which has not been calibrated out. The problem is further compounded by the possibility that an EO/IR sensor may not see exactly the same set of targets as a microwave radar. In order to better understand the problems involved in performing the fusion of metric information from EO/IR satellite measurements with active microwave radar measurements, we have undertaken a study of this data fusion issue and of the associated data processing techniques. To carry out this analysis, we have made use of high fidelity simulations to model the radar observations from a missile target and the observations of the same simulated target, as gathered by a constellation of satellites. In the paper, we discuss the improvements seen in our tests when fusing the state vectors, along with the improvements in sensor bias estimation. The limitations in performance due to the differing phenomenology between IR and microwave radar are discussed as well.

Robotics and local fusion

NASA Astrophysics Data System (ADS)

Emmerman, Philip J.

2005-05-01

Teams of robots or mixed teams of warfighters and robots on reconnaissance and other missions can benefit greatly from a local fusion station. A local fusion station is defined here as a small mobile processor with interfaces to enable the ingestion of multiple heterogeneous sensor data and information streams, including blue force tracking data. These data streams are fused and integrated with contextual information (terrain features, weather, maps, dynamic background features, etc.), and displayed or processed to provide real time situational awareness to the robot controller or to the robots themselves. These blue and red force fusion applications remove redundancies, lessen ambiguities, correlate, aggregate, and integrate sensor information with context such as high resolution terrain. Applications such as safety, team behavior, asset control, training, pattern analysis, etc. can be generated or enhanced by these fusion stations. This local fusion station should also enable the interaction between these local units and a global information world.

Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient.

PubMed

Shi, Fengjian; Su, Xiaoyan; Qian, Hong; Yang, Ning; Han, Wenhua

2017-10-16

In order to meet the higher accuracy and system reliability requirements, the information fusion for multi-sensor systems is an increasing concern. Dempster-Shafer evidence theory (D-S theory) has been investigated for many applications in multi-sensor information fusion due to its flexibility in uncertainty modeling. However, classical evidence theory assumes that the evidence is independent of each other, which is often unrealistic. Ignoring the relationship between the evidence may lead to unreasonable fusion results, and even lead to wrong decisions. This assumption severely prevents D-S evidence theory from practical application and further development. In this paper, an innovative evidence fusion model to deal with dependent evidence based on rank correlation coefficient is proposed. The model first uses rank correlation coefficient to measure the dependence degree between different evidence. Then, total discount coefficient is obtained based on the dependence degree, which also considers the impact of the reliability of evidence. Finally, the discount evidence fusion model is presented. An example is illustrated to show the use and effectiveness of the proposed method.

Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient

PubMed Central

Su, Xiaoyan; Qian, Hong; Yang, Ning; Han, Wenhua

2017-01-01

In order to meet the higher accuracy and system reliability requirements, the information fusion for multi-sensor systems is an increasing concern. Dempster–Shafer evidence theory (D–S theory) has been investigated for many applications in multi-sensor information fusion due to its flexibility in uncertainty modeling. However, classical evidence theory assumes that the evidence is independent of each other, which is often unrealistic. Ignoring the relationship between the evidence may lead to unreasonable fusion results, and even lead to wrong decisions. This assumption severely prevents D–S evidence theory from practical application and further development. In this paper, an innovative evidence fusion model to deal with dependent evidence based on rank correlation coefficient is proposed. The model first uses rank correlation coefficient to measure the dependence degree between different evidence. Then, total discount coefficient is obtained based on the dependence degree, which also considers the impact of the reliability of evidence. Finally, the discount evidence fusion model is presented. An example is illustrated to show the use and effectiveness of the proposed method. PMID:29035341

Data-Fusion for a Vision-Aided Radiological Detection System: Sensor dependence and Source Tracking

NASA Astrophysics Data System (ADS)

Stadnikia, Kelsey; Martin, Allan; Henderson, Kristofer; Koppal, Sanjeev; Enqvist, Andreas

2018-01-01

The University of Florida is taking a multidisciplinary approach to fuse the data between 3D vision sensors and radiological sensors in hopes of creating a system capable of not only detecting the presence of a radiological threat, but also tracking it. The key to developing such a vision-aided radiological detection system, lies in the count rate being inversely dependent on the square of the distance. Presented in this paper are the results of the calibration algorithm used to predict the location of the radiological detectors based on 3D distance from the source to the detector (vision data) and the detectors count rate (radiological data). Also presented are the results of two correlation methods used to explore source tracking.

Dynamic reweighting of three modalities for sensor fusion.

PubMed

Hwang, Sungjae; Agada, Peter; Kiemel, Tim; Jeka, John J

2014-01-01

We simultaneously perturbed visual, vestibular and proprioceptive modalities to understand how sensory feedback is re-weighted so that overall feedback remains suited to stabilizing upright stance. Ten healthy young subjects received an 80 Hz vibratory stimulus to their bilateral Achilles tendons (stimulus turns on-off at 0.28 Hz), a ± 1 mA binaural monopolar galvanic vestibular stimulus at 0.36 Hz, and a visual stimulus at 0.2 Hz during standing. The visual stimulus was presented at different amplitudes (0.2, 0.8 deg rotation about ankle axis) to measure: the change in gain (weighting) to vision, an intramodal effect; and a change in gain to vibration and galvanic vestibular stimulation, both intermodal effects. The results showed a clear intramodal visual effect, indicating a de-emphasis on vision when the amplitude of visual stimulus increased. At the same time, an intermodal visual-proprioceptive reweighting effect was observed with the addition of vibration, which is thought to change proprioceptive inputs at the ankles, forcing the nervous system to rely more on vision and vestibular modalities. Similar intermodal effects for visual-vestibular reweighting were observed, suggesting that vestibular information is not a "fixed" reference, but is dynamically adjusted in the sensor fusion process. This is the first time, to our knowledge, that the interplay between the three primary modalities for postural control has been clearly delineated, illustrating a central process that fuses these modalities for accurate estimates of self-motion.

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

PubMed Central

Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

2014-01-01

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs. PMID:25587878

Dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks.

PubMed

Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

2014-11-26

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

Aerospace Sensor Systems: From Sensor Development To Vehicle Application

NASA Technical Reports Server (NTRS)

Hunter, Gary W.

2008-01-01

This paper presents an overview of years of sensor system development and application for aerospace systems. The emphasis of this work is on developing advanced capabilities for measurement and control of aeropropulsion and crew vehicle systems as well as monitoring the safety of those systems. Specific areas of work include chemical species sensors, thin film thermocouples and strain gages, heat flux gages, fuel gages, SiC based electronic devices and sensors, space qualified electronics, and MicroElectroMechanical Systems (MEMS) as well as integrated and multifunctional sensor systems. Each sensor type has its own technical challenges related to integration and reliability in a given application. The general approach has been to develop base sensor technology using microfabrication techniques, integrate sensors with "smart" hardware and software, and demonstrate those systems in a range of aerospace applications. Descriptions of the sensor elements, their integration into sensors systems, and examples of sensor system applications will be discussed. Finally, suggestions related to the future of sensor technology will be given. It is concluded that smart micro/nano sensor technology can revolutionize aerospace applications, but significant challenges exist in maturing the technology and demonstrating its value in real-life applications.

Biometric image enhancement using decision rule based image fusion techniques

NASA Astrophysics Data System (ADS)

Sagayee, G. Mary Amirtha; Arumugam, S.

2010-02-01

Introducing biometrics into information systems may result in considerable benefits. Most of the researchers confirmed that the finger print is widely used than the iris or face and more over it is the primary choice for most privacy concerned applications. For finger prints applications, choosing proper sensor is at risk. The proposed work deals about, how the image quality can be improved by introducing image fusion technique at sensor levels. The results of the images after introducing the decision rule based image fusion technique are evaluated and analyzed with its entropy levels and root mean square error.

Compact survey and inspection day/night image sensor suite for small unmanned aircraft systems (EyePod)

NASA Astrophysics Data System (ADS)

Bird, Alan; Anderson, Scott A.; Linne von Berg, Dale; Davidson, Morgan; Holt, Niel; Kruer, Melvin; Wilson, Michael L.

2010-04-01

EyePod is a compact survey and inspection day/night imaging sensor suite for small unmanned aircraft systems (UAS). EyePod generates georeferenced image products in real-time from visible near infrared (VNIR) and long wave infrared (LWIR) imaging sensors and was developed under the ONR funded FEATHAR (Fusion, Exploitation, Algorithms, and Targeting for High-Altitude Reconnaissance) program. FEATHAR is being directed and executed by the Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL) and FEATHAR's goal is to develop and test new tactical sensor systems specifically designed for small manned and unmanned platforms (payload weight < 50 lbs). The EyePod suite consists of two VNIR/LWIR (day/night) gimbaled sensors that, combined, provide broad area survey and focused inspection capabilities. Each EyePod sensor pairs an HD visible EO sensor with a LWIR bolometric imager providing precision geo-referenced and fully digital EO/IR NITFS output imagery. The LWIR sensor is mounted to a patent-pending jitter-reduction stage to correct for the high-frequency motion typically found on small aircraft and unmanned systems. Details will be presented on both the wide-area and inspection EyePod sensor systems, their modes of operation, and results from recent flight demonstrations.

Anchor Node Localization for Wireless Sensor Networks Using Video and Compass Information Fusion

PubMed Central

Pescaru, Dan; Curiac, Daniel-Ioan

2014-01-01

Distributed sensing, computing and communication capabilities of wireless sensor networks require, in most situations, an efficient node localization procedure. In the case of random deployments in harsh or hostile environments, a general localization process within global coordinates is based on a set of anchor nodes able to determine their own position using GPS receivers. In this paper we propose another anchor node localization technique that can be used when GPS devices cannot accomplish their mission or are considered to be too expensive. This novel technique is based on the fusion of video and compass data acquired by the anchor nodes and is especially suitable for video- or multimedia-based wireless sensor networks. For these types of wireless networks the presence of video cameras is intrinsic, while the presence of digital compasses is also required for identifying the cameras' orientations. PMID:24594614

Estimation of tool wear during CNC milling using neural network-based sensor fusion

NASA Astrophysics Data System (ADS)

Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

2007-01-01

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

The effect of multispectral image fusion enhancement on human efficiency.

PubMed

Bittner, Jennifer L; Schill, M Trent; Mohd-Zaid, Fairul; Blaha, Leslie M

2017-01-01

The visual system can be highly influenced by changes to visual presentation. Thus, numerous techniques have been developed to augment imagery in an attempt to improve human perception. The current paper examines the potential impact of one such enhancement, multispectral image fusion, where imagery captured in varying spectral bands (e.g., visible, thermal, night vision) is algorithmically combined to produce an output to strengthen visual perception. We employ ideal observer analysis over a series of experimental conditions to (1) establish a framework for testing the impact of image fusion over the varying aspects surrounding its implementation (e.g., stimulus content, task) and (2) examine the effectiveness of fusion on human information processing efficiency in a basic application. We used a set of rotated Landolt C images captured with a number of individual sensor cameras and combined across seven traditional fusion algorithms (e.g., Laplacian pyramid, principal component analysis, averaging) in a 1-of-8 orientation task. We found that, contrary to the idea of fused imagery always producing a greater impact on perception, single-band imagery can be just as influential. Additionally, efficiency data were shown to fluctuate based on sensor combination instead of fusion algorithm, suggesting the need for examining multiple factors to determine the success of image fusion. Our use of ideal observer analysis, a popular technique from the vision sciences, provides not only a standard for testing fusion in direct relation to the visual system but also allows for comparable examination of fusion across its associated problem space of application.

The role of data fusion in predictive maintenance using digital twin

NASA Astrophysics Data System (ADS)

Liu, Zheng; Meyendorf, Norbert; Mrad, Nezih

2018-04-01

Modern aerospace industry is migrating from reactive to proactive and predictive maintenance to increase platform operational availability and efficiency, extend its useful life cycle and reduce its life cycle cost. Multiphysics modeling together with data-driven analytics generate a new paradigm called "Digital Twin." The digital twin is actually a living model of the physical asset or system, which continually adapts to operational changes based on the collected online data and information, and can forecast the future of the corresponding physical counterpart. This paper reviews the overall framework to develop a digital twin coupled with the industrial Internet of Things technology to advance aerospace platforms autonomy. Data fusion techniques particularly play a significant role in the digital twin framework. The flow of information from raw data to high-level decision making is propelled by sensor-to-sensor, sensor-to-model, and model-to-model fusion. This paper further discusses and identifies the role of data fusion in the digital twin framework for aircraft predictive maintenance.

The application of machine learning in multi sensor data fusion for activity recognition in mobile device space

NASA Astrophysics Data System (ADS)

Marhoubi, Asmaa H.; Saravi, Sara; Edirisinghe, Eran A.

2015-05-01

The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light Sensor, Proximity Sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device's behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion.

Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E.

2013-05-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will

Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

2013-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will

A novel framework of tissue membrane systems for image fusion.

PubMed

Zhang, Zulin; Yi, Xinzhong; Peng, Hong

2014-01-01

This paper proposes a tissue membrane system-based framework to deal with the optimal image fusion problem. A spatial domain fusion algorithm is given, and a tissue membrane system of multiple cells is used as its computing framework. Based on the multicellular structure and inherent communication mechanism of the tissue membrane system, an improved velocity-position model is developed. The performance of the fusion framework is studied with comparison of several traditional fusion methods as well as genetic algorithm (GA)-based and differential evolution (DE)-based spatial domain fusion methods. Experimental results show that the proposed fusion framework is superior or comparable to the other methods and can be efficiently used for image fusion.

Design Considerations for Clean QED Fusion Propulsion Systems

NASA Astrophysics Data System (ADS)

Bussard, Robert W.; Jameson, Lorin W.

1994-07-01

The direct production of electric power appears possible from fusion reactions between fuels whose products consist solely of charged particles and thus do not present radiation hazards from energetic neutron production, as do reactions involving deuteron-bearing fuels. Among these are the fuels p, 11B, 3He, and 6Li. All of these can be ``burned'' in inertial-electrostatic-fusion (IEF) devices to power QED fusion-electric rocket engines. These IEF sources provide direct-converted electrical power at high voltage (MeV) to drive e-beams for efficient propellant heating to extreme temperatures, with resulting high specific impulse performance capabilities. IEF/QED engine systems using p11B can outperform all other advanced concepts for controlled fusion propulsion by 2-3 orders of magnitude, while 6Li6Li fusion yields one order of magnitude less advance. Either of these fusion rocket propulsion systems can provide very rapid transit for solar system missions, with high payload fractions in single-stage vehicles. The 3He3He reaction can not be used practically for direct electric conversion because of the wide spread in energy of its fusion products. However, it may eventually prove useful for thermal/electrical power generation in central station power plants, or for direct-fusion-product (DFP) propellant heatingin advanced deep-space rocket engines.

Hierarchical adaptation scheme for multiagent data fusion and resource management in situation analysis

NASA Astrophysics Data System (ADS)

Benaskeur, Abder R.; Roy, Jean

2001-08-01

Sensor Management (SM) has to do with how to best manage, coordinate and organize the use of sensing resources in a manner that synergistically improves the process of data fusion. Based on the contextual information, SM develops options for collecting further information, allocates and directs the sensors towards the achievement of the mission goals and/or tunes the parameters for the realtime improvement of the effectiveness of the sensing process. Conscious of the important role that SM has to play in modern data fusion systems, we are currently studying advanced SM Concepts that would help increase the survivability of the current Halifax and Iroquois Class ships, as well as their possible future upgrades. For this purpose, a hierarchical scheme has been proposed for data fusion and resource management adaptation, based on the control theory and within the process refinement paradigm of the JDL data fusion model, and taking into account the multi-agent model put forward by the SASS Group for the situation analysis process. The novelty of this work lies in the unified framework that has been defined for tackling the adaptation of both the fusion process and the sensor/weapon management.

Intelligent data processing of an ultrasonic sensor system for pattern recognition improvements

NASA Astrophysics Data System (ADS)

Na, Seung You; Park, Min-Sang; Hwang, Won-Gul; Kee, Chang-Doo

1999-05-01

Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

Trust metrics in information fusion

NASA Astrophysics Data System (ADS)

Blasch, Erik

2014-05-01

Trust is an important concept for machine intelligence and is not consistent across many applications. In this paper, we seek to understand trust from a variety of factors: humans, sensors, communications, intelligence processing algorithms and human-machine displays of information. In modeling the various aspects of trust, we provide an example from machine intelligence that supports the various attributes of measuring trust such as sensor accuracy, communication timeliness, machine processing confidence, and display throughput to convey the various attributes that support user acceptance of machine intelligence results. The example used is fusing video and text whereby an analyst needs trust information in the identified imagery track. We use the proportional conflict redistribution rule as an information fusion technique that handles conflicting data from trusted and mistrusted sources. The discussion of the many forms of trust explored in the paper seeks to provide a systems-level design perspective for information fusion trust quantification.

Cooperative angle-only orbit initialization via fusion of admissible areas

NASA Astrophysics Data System (ADS)

Jia, Bin; Pham, Khanh; Blasch, Erik; Chen, Genshe; Shen, Dan; Wang, Zhonghai

2017-05-01

For the short-arc angle only orbit initialization problem, the admissible area is often used. However, the accuracy using a single sensor is often limited. For high value space objects, it is desired to achieve more accurate results. Fortunately, multiple sensors, which are dedicated to space situational awareness, are available. The work in this paper uses multiple sensors' information to cooperatively initialize the orbit based on the fusion of multiple admissible areas. Both the centralized fusion and decentralized fusion are discussed. Simulation results verify the expectation that the orbit initialization accuracy is improved by using information from multiple sensors.

Improving Planetary Rover Attitude Estimation via MEMS Sensor Characterization

PubMed Central

Hidalgo, Javier; Poulakis, Pantelis; Köhler, Johan; Del-Cerro, Jaime; Barrientos, Antonio

2012-01-01

Micro Electro-Mechanical Systems (MEMS) are currently being considered in the space sector due to its suitable level of performance for spacecrafts in terms of mechanical robustness with low power consumption, small mass and size, and significant advantage in system design and accommodation. However, there is still a lack of understanding regarding the performance and testing of these new sensors, especially in planetary robotics. This paper presents what is missing in the field: a complete methodology regarding the characterization and modeling of MEMS sensors with direct application. A reproducible and complete approach including all the intermediate steps, tools and laboratory equipment is described. The process of sensor error characterization and modeling through to the final integration in the sensor fusion scheme is explained with detail. Although the concept of fusion is relatively easy to comprehend, carefully characterizing and filtering sensor information is not an easy task and is essential for good performance. The strength of the approach has been verified with representative tests of novel high-grade MEMS inertia sensors and exemplary planetary rover platforms with promising results. PMID:22438761

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

PubMed

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

How Magnetic Disturbance Influences the Attitude and Heading in Magnetic and Inertial Sensor-Based Orientation Estimation.

PubMed

Fan, Bingfei; Li, Qingguo; Liu, Tao

2017-12-28

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method.

Blob-level active-passive data fusion for Benthic classification

NASA Astrophysics Data System (ADS)

Park, Joong Yong; Kalluri, Hemanth; Mathur, Abhinav; Ramnath, Vinod; Kim, Minsu; Aitken, Jennifer; Tuell, Grady

2012-06-01

We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.

Context Representation and Fusion: Advancements and Opportunities

PubMed Central

Khattak, Asad Masood; Akbar, Noman; Aazam, Mohammad; Ali, Taqdir; Khan, Adil Mehmood; Jeon, Seokhee; Hwang, Myunggwon; Lee, Sungyoung

2014-01-01

The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems. PMID:24887042

Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach.

PubMed

Liu, Mengyun; Chen, Ruizhi; Li, Deren; Chen, Yujin; Guo, Guangyi; Cao, Zhipeng; Pan, Yuanjin

2017-12-08

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for

Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

PubMed Central

Chen, Ruizhi; Li, Deren; Chen, Yujin; Guo, Guangyi; Cao, Zhipeng

2017-01-01

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for

Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, Brian; Manipon, Gerald; Hua, Hook; Fetzer, Eric

2014-05-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map-reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in a hybrid Cloud (private eucalyptus & public Amazon). Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Multi-year datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the

A Structured Light Sensor System for Tree Inventory

NASA Technical Reports Server (NTRS)

Chien, Chiun-Hong; Zemek, Michael C.

2000-01-01

Tree Inventory is referred to measurement and estimation of marketable wood volume in a piece of land or forest for purposes such as investment or for loan applications. Exist techniques rely on trained surveyor conducting measurements manually using simple optical or mechanical devices, and hence are time consuming subjective and error prone. The advance of computer vision techniques makes it possible to conduct automatic measurements that are more efficient, objective and reliable. This paper describes 3D measurements of tree diameters using a uniquely designed ensemble of two line laser emitters rigidly mounted on a video camera. The proposed laser camera system relies on a fixed distance between two parallel laser planes and projections of laser lines to calculate tree diameters. Performance of the laser camera system is further enhanced by fusion of information induced from structured lighting and that contained in video images. Comparison will be made between the laser camera sensor system and a stereo vision system previously developed for measurements of tree diameters.

Sensor-scheduling simulation of disparate sensors for Space Situational Awareness

NASA Astrophysics Data System (ADS)

Hobson, T.; Clarkson, I.

2011-09-01

The art and science of space situational awareness (SSA) has been practised and developed from the time of Sputnik. However, recent developments, such as the accelerating pace of satellite launch, the proliferation of launch capable agencies, both commercial and sovereign, and recent well-publicised collisions involving man-made space objects, has further magnified the importance of timely and accurate SSA. The United States Strategic Command (USSTRATCOM) operates the Space Surveillance Network (SSN), a global network of sensors tasked with maintaining SSA. The rapidly increasing number of resident space objects will require commensurate improvements in the SSN. Sensors are scarce resources that must be scheduled judiciously to obtain measurements of maximum utility. Improvements in sensor scheduling and fusion, can serve to reduce the number of additional sensors that may be required. Recently, Hill et al. [1] have proposed and developed a simulation environment named TASMAN (Tasking Autonomous Sensors in a Multiple Application Network) to enable testing of alternative scheduling strategies within a simulated multi-sensor, multi-target environment. TASMAN simulates a high-fidelity, hardware-in-the-loop system by running multiple machines with different roles in parallel. At present, TASMAN is limited to simulations involving electro-optic sensors. Its high fidelity is at once a feature and a limitation, since supercomputing is required to run simulations of appreciable scale. In this paper, we describe an alternative, modular and scalable SSA simulation system that can extend the work of Hill et al with reduced complexity, albeit also with reduced fidelity. The tool has been developed in MATLAB and therefore can be run on a very wide range of computing platforms. It can also make use of MATLAB’s parallel processing capabilities to obtain considerable speed-up. The speed and flexibility so obtained can be used to quickly test scheduling algorithms even with a

Wastewater quality monitoring system using sensor fusion and machine learning techniques.

PubMed

Qin, Xusong; Gao, Furong; Chen, Guohua

2012-03-15

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System

PubMed Central

Wang, Cheng; Wang, Xiangdong; Long, Zhou; Yuan, Jing; Qian, Yueliang; Li, Jintao

2016-01-01

Most existing wearable gait analysis methods focus on the analysis of data obtained from inertial sensors. This paper proposes a novel, low-cost, wireless and wearable gait analysis system which uses microphone sensors to collect footstep sound signals during walking. This is the first time a microphone sensor is used as a wearable gait analysis device as far as we know. Based on this system, a gait analysis algorithm for estimating the temporal parameters of gait is presented. The algorithm fully uses the fusion of two feet footstep sound signals and includes three stages: footstep detection, heel-strike event and toe-on event detection, and calculation of gait temporal parameters. Experimental results show that with a total of 240 data sequences and 1732 steps collected using three different gait data collection strategies from 15 healthy subjects, the proposed system achieves an average 0.955 F1-measure for footstep detection, an average 94.52% accuracy rate for heel-strike detection and 94.25% accuracy rate for toe-on detection. Using these detection results, nine temporal related gait parameters are calculated and these parameters are consistent with their corresponding normal gait temporal parameters and labeled data calculation results. The results verify the effectiveness of our proposed system and algorithm for temporal gait parameter estimation. PMID:27999321

A review of potential image fusion methods for remote sensing-based irrigation management: Part II

USDA-ARS?s Scientific Manuscript database

Satellite-based sensors provide data at either greater spectral and coarser spatial resolutions, or lower spectral and finer spatial resolutions due to complementary spectral and spatial characteristics of optical sensor systems. In order to overcome this limitation, image fusion has been suggested ...

Neuro-Analogical Gate Tuning of Trajectory Data Fusion for a Mecanum-Wheeled Special Needs Chair

PubMed Central

ElSaharty, M. A.; zakzouk, Ezz Eldin

2017-01-01

Trajectory tracking of mobile wheeled chairs using internal shaft encoder and inertia measurement unit(IMU), exhibits several complications and accumulated errors in the tracking process due to wheel slippage, offset drift and integration approximations. These errors can be realized when comparing localization results from such sensors with a camera tracking system. In long trajectory tracking, such errors can accumulate and result in significant deviations which make data from these sensors unreliable for tracking. Meanwhile the utilization of an external camera tracking system is not always a feasible solution depending on the implementation environment. This paper presents a novel sensor fusion method that combines the measurements of internal sensors to accurately predict the location of the wheeled chair in an environment. The method introduces a new analogical OR gate structured with tuned parameters using multi-layer feedforward neural network denoted as “Neuro-Analogical Gate” (NAG). The resulting system minimize any deviation error caused by the sensors, thus accurately tracking the wheeled chair location without the requirement of an external camera tracking system. The fusion methodology has been tested with a prototype Mecanum wheel-based chair, and significant improvement over tracking response, error and performance has been observed. PMID:28045973

Adaptive multisensor fusion for planetary exploration rovers

NASA Technical Reports Server (NTRS)

Collin, Marie-France; Kumar, Krishen; Pampagnin, Luc-Henri

1992-01-01

The purpose of the adaptive multisensor fusion system currently being designed at NASA/Johnson Space Center is to provide a robotic rover with assured vision and safe navigation capabilities during robotic missions on planetary surfaces. Our approach consists of using multispectral sensing devices ranging from visible to microwave wavelengths to fulfill the needs of perception for space robotics. Based on the illumination conditions and the sensors capabilities knowledge, the designed perception system should automatically select the best subset of sensors and their sensing modalities that will allow the perception and interpretation of the environment. Then, based on reflectance and emittance theoretical models, the sensor data are fused to extract the physical and geometrical surface properties of the environment surface slope, dielectric constant, temperature and roughness. The theoretical concepts, the design and first results of the multisensor perception system are presented.

Information Fusion of Conflicting Input Data.

PubMed

Mönks, Uwe; Dörksen, Helene; Lohweg, Volker; Hübner, Michael

2016-10-29

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μ BalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.

Information Fusion of Conflicting Input Data

PubMed Central

Mönks, Uwe; Dörksen, Helene; Lohweg, Volker; Hübner, Michael

2016-01-01

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible. PMID:27801874

Engineering of the Magnetized Target Fusion Propulsion System

NASA Technical Reports Server (NTRS)

Statham, G.; White, S.; Adams, R. B.; Thio, Y. C. F.; Santarius, J.; Alexander, R.; Chapman, J.; Fincher, S.; Philips, A.; Polsgrove, T.

2003-01-01

Engineering details are presented for a magnetized target fusion (MTF) propulsion system designed to support crewed missions to the outer solar system. Basic operation of an MTF propulsion system is introduced. Structural, thermal, radiation-management and electrical design details are presented. The propellant storage and supply system design is also presented. A propulsion system mass estimate and associated performance figures are given. The advantages of helium-3 as a fusion fuel for an advanced MTF system are discussed.

A fusion approach for coarse-to-fine target recognition

NASA Astrophysics Data System (ADS)

Folkesson, Martin; Grönwall, Christina; Jungert, Erland

2006-04-01

A fusion approach in a query based information system is presented. The system is designed for querying multimedia data bases, and here applied to target recognition using heterogeneous data sources. The recognition process is coarse-to-fine, with an initial attribute estimation step and a following matching step. Several sensor types and algorithms are involved in each of these two steps. An independence of the matching results, on the origin of the estimation results, is observed. It allows for distribution of data between algorithms in an intermediate fusion step, without risk of data incest. This increases the overall chance of recognising the target. An implementation of the system is described.

Multimodal biometric system using rank-level fusion approach.

PubMed

Monwar, Md Maruf; Gavrilova, Marina L

2009-08-01

In many real-world applications, unimodal biometric systems often face significant limitations due to sensitivity to noise, intraclass variability, data quality, nonuniversality, and other factors. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator. This paper presents an effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fisher's linear discriminant methods for individual matchers (face, ear, and signature) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers. The ranks of individual matchers are combined using the highest rank, Borda count, and logistic regression approaches. The results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data. Insights on multibiometric design using rank-level fusion and its performance on a variety of biometric databases are discussed in the concluding section.

How Magnetic Disturbance Influences the Attitude and Heading in Magnetic and Inertial Sensor-Based Orientation Estimation

PubMed Central

Li, Qingguo

2017-01-01

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method. PMID:29283432

Sensor Fusion for Nuclear Proliferation Activity Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adel Ghanem, Ph D

2007-03-30

The objective of Phase 1 of this STTR project is to demonstrate a Proof-of-Concept (PoC) of the Geo-Rad system that integrates a location-aware SmartTag (made by ZonTrak) and a radiation detector (developed by LLNL). It also includes the ability to transmit the collected radiation data and location information to the ZonTrak server (ZonService). The collected data is further transmitted to a central server at LLNL (the Fusion Server) to be processed in conjunction with overhead imagery to generate location estimates of nuclear proliferation and radiation sources.

Integrated multi-sensor package (IMSP) for unmanned vehicle operations

NASA Astrophysics Data System (ADS)

Crow, Eddie C.; Reichard, Karl; Rogan, Chris; Callen, Jeff; Seifert, Elwood

2007-10-01

This paper describes recent efforts to develop integrated multi-sensor payloads for small robotic platforms for improved operator situational awareness and ultimately for greater robot autonomy. The focus is on enhancements to perception through integration of electro-optic, acoustic, and other sensors for navigation and inspection. The goals are to provide easier control and operation of the robot through fusion of multiple sensor outputs, to improve interoperability of the sensor payload package across multiple platforms through the use of open standards and architectures, and to reduce integration costs by embedded sensor data processing and fusion within the sensor payload package. The solutions investigated in this project to be discussed include: improved capture, processing and display of sensor data from multiple, non-commensurate sensors; an extensible architecture to support plug and play of integrated sensor packages; built-in health, power and system status monitoring using embedded diagnostics/prognostics; sensor payload integration into standard product forms for optimized size, weight and power; and the use of the open Joint Architecture for Unmanned Systems (JAUS)/ Society of Automotive Engineers (SAE) AS-4 interoperability standard. This project is in its first of three years. This paper will discuss the applicability of each of the solutions in terms of its projected impact to reducing operational time for the robot and teleoperator.

Towards a Unified Approach to Information Integration - A review paper on data/information fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitney, Paul D.; Posse, Christian; Lei, Xingye C.

2005-10-14

Information or data fusion of data from different sources are ubiquitous in many applications, from epidemiology, medical, biological, political, and intelligence to military applications. Data fusion involves integration of spectral, imaging, text, and many other sensor data. For example, in epidemiology, information is often obtained based on many studies conducted by different researchers at different regions with different protocols. In the medical field, the diagnosis of a disease is often based on imaging (MRI, X-Ray, CT), clinical examination, and lab results. In the biological field, information is obtained based on studies conducted on many different species. In military field, informationmore » is obtained based on data from radar sensors, text messages, chemical biological sensor, acoustic sensor, optical warning and many other sources. Many methodologies are used in the data integration process, from classical, Bayesian, to evidence based expert systems. The implementation of the data integration ranges from pure software design to a mixture of software and hardware. In this review we summarize the methodologies and implementations of data fusion process, and illustrate in more detail the methodologies involved in three examples. We propose a unified multi-stage and multi-path mapping approach to the data fusion process, and point out future prospects and challenges.« less

Laser sensor system documentation.

DOT National Transportation Integrated Search

2017-03-01

Phase 1 of TxDOT Project 0-6873, True Road Surface Deflection Measuring Device, developed a : laser sensor system based on several sensors mounted on a rigid beam. : This sensor system remains with CTR currently, as the project is moving into Phase 2...

Accurate 3D Positioning for a Mobile Platform in Non-Line-of-Sight Scenarios Based on IMU/Magnetometer Sensor Fusion.

PubMed

Hellmers, Hendrik; Kasmi, Zakaria; Norrdine, Abdelmoumen; Eichhorn, Andreas

2018-01-04

In recent years, a variety of real-time applications benefit from services provided by localization systems due to the advent of sensing and communication technologies. Since the Global Navigation Satellite System (GNSS) enables localization only outside buildings, applications for indoor positioning and navigation use alternative technologies. Ultra Wide Band Signals (UWB), Wireless Local Area Network (WLAN), ultrasonic or infrared are common examples. However, these technologies suffer from fading and multipath effects caused by objects and materials in the building. In contrast, magnetic fields are able to pass through obstacles without significant propagation errors, i.e. in Non-Line of Sight Scenarios (NLoS). The aim of this work is to propose a novel indoor positioning system based on artificially generated magnetic fields in combination with Inertial Measurement Units (IMUs). In order to reach a better coverage, multiple coils are used as reference points. A basic algorithm for three-dimensional applications is demonstrated as well as evaluated in this article. The established system is then realized by a sensor fusion principle as well as a kinematic motion model on the basis of a Kalman filter. Furthermore, a pressure sensor is used in combination with an adaptive filtering method to reliably estimate the platform's altitude.

Munc13-4 functions as a Ca2+ sensor for homotypic secretory granule fusion to generate endosomal exocytic vacuoles

PubMed Central

Woo, Sang Su; James, Declan J.; Martin, Thomas F. J.

2017-01-01

Munc13-4 is a Ca2+-dependent SNARE (soluble N-ethylmaleimide–sensitive factor attachment protein receptor)- and phospholipid-binding protein that localizes to and primes secretory granules (SGs) for Ca2+-evoked secretion in various secretory cells. Studies in mast cell–like RBL-2H3 cells provide direct evidence that Munc13–4 with its two Ca2+-binding C2 domains functions as a Ca2+ sensor for SG exocytosis. Unexpectedly, Ca2+ stimulation also generated large (>2.4 μm in diameter) Munc13-4+/Rab7+/Rab11+ endosomal vacuoles. Vacuole generation involved the homotypic fusion of Munc13-4+/Rab7+ SGs, followed by a merge with Rab11+ endosomes, and depended on Ca2+ binding to Munc13-4. Munc13-4 promoted the Ca2+-stimulated fusion of VAMP8-containing liposomes with liposomes containing exocytic or endosomal Q-SNAREs and directly interacted with late endosomal SNARE complexes. Thus Munc13-4 is a tethering/priming factor and Ca2+ sensor for both heterotypic SG-plasma membrane and homotypic SG-SG fusion. Total internal reflection fluorescence microscopy imaging revealed that vacuoles were exocytic and mediated secretion of β-hexosaminidase and cytokines accompanied by Munc13-4 diffusion onto the plasma membrane. The results provide new molecular insights into the mechanism of multigranular compound exocytosis commonly observed in various secretory cells. PMID:28100639

Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na

2014-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:24982935

Toward sensor modular autonomy for persistent land intelligence surveillance and reconnaissance (ISR)

NASA Astrophysics Data System (ADS)

Thomas, Paul A.; Marshall, Gillian; Faulkner, David; Kent, Philip; Page, Scott; Islip, Simon; Oldfield, James; Breckon, Toby P.; Kundegorski, Mikolaj E.; Clark, David J.; Styles, Tim

2016-05-01

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. Any automation in the system has traditionally involved bespoke design of centralised systems that are highly specific for the assets/targets/environment under consideration, resulting in complex, non-flexible systems that exhibit poor interoperability. We address a concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules have the ability to make low-level decisions on their own in order to fulfil a higher-level objective, and plug in, with the minimum of preconfiguration, to a High Level Decision Making Module (HLDMM) through a middleware integration layer. The dual requisites of autonomy and interoperability create challenges around information fusion and asset management in an autonomous hierarchical system, which are addressed in this work. This paper presents the results of a demonstration system, known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT), which was shown in realistic base protection scenarios with live sensors and targets. The SAPIENT system performed sensor cueing, intelligent fusion, sensor tasking, target hand-off and compensation for compromised sensors, without human control, and enabled rapid integration of ISR assets at the time of system deployment, rather than at design-time. Potential benefits include rapid interoperability for coalition operations, situation understanding with low operator cognitive burden and autonomous sensor management in heterogenous sensor systems.

Energy Systems Sensor Laboratory | Energy Systems Integration Facility |

Science.gov Websites

NREL Sensor Laboratory Energy Systems Sensor Laboratory The Energy Systems Integration Facility's Energy Systems Sensor Laboratory is designed to support research, development, testing, and evaluation of advanced hydrogen sensor technologies to support the needs of the emerging hydrogen

RadMAP: The Radiological Multi-sensor Analysis Platform

NASA Astrophysics Data System (ADS)

Bandstra, Mark S.; Aucott, Timothy J.; Brubaker, Erik; Chivers, Daniel H.; Cooper, Reynold J.; Curtis, Joseph C.; Davis, John R.; Joshi, Tenzing H.; Kua, John; Meyer, Ross; Negut, Victor; Quinlan, Michael; Quiter, Brian J.; Srinivasan, Shreyas; Zakhor, Avideh; Zhang, Richard; Vetter, Kai

2016-12-01

The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

Experimental measurement of oil-water two-phase flow by data fusion of electrical tomography sensors and venturi tube

NASA Astrophysics Data System (ADS)

Liu, Yinyan; Deng, Yuchi; Zhang, Maomao; Yu, Peining; Li, Yi

2017-09-01

Oil-water two-phase flows are commonly found in the production processes of the petroleum industry. Accurate online measurement of flow rates is crucial to ensure the safety and efficiency of oil exploration and production. A research team from Tsinghua University has developed an experimental apparatus for multiphase flow measurement based on an electrical capacitance tomography (ECT) sensor, an electrical resistance tomography (ERT) sensor, and a venturi tube. This work presents the phase fraction and flow rate measurements of oil-water two-phase flows based on the developed apparatus. Full-range phase fraction can be obtained by the combination of the ECT sensor and the ERT sensor. By data fusion of differential pressures measured by venturi tube and the phase fraction, the total flow rate and single-phase flow rate can be calculated. Dynamic experiments were conducted on the multiphase flow loop in horizontal and vertical pipelines and at various flow rates.

Multisource information fusion applied to ship identification for the recognized maritime picture

NASA Astrophysics Data System (ADS)

Simard, Marc-Alain; Lefebvre, Eric; Helleur, Christopher

2000-04-01

The Recognized Maritime Picture (RMP) is defined as a composite picture of activity over a maritime area of interest. In simplistic terms, building an RAMP comes down to finding if an object of interest, a ship in our case, is there or not, determining what it is, determining what it is doing and determining if some type of follow-on action is required. The Canadian Department of National Defence currently has access to or may, in the near future, have access to a number of civilians, military and allied information or sensor systems to accomplish these purposes. These systems include automatic self-reporting positional systems, air patrol surveillance systems, high frequency surface radars, electronic intelligence systems, radar space systems and high frequency direction finding sensors. The ability to make full use of these systems is limited by the existing capability to fuse data from all sources in a timely, accurate and complete manner. This paper presents an information fusion systems under development that correlates and fuses these information and sensor data sources. This fusion system, named Adaptive Fuzzy Logic Correlator, correlates the information in batch but fuses and constructs ship tracks sequentially. It applies standard Kalman filter techniques and fuzzy logic correlation techniques. We propose a set of recommendations that should improve the ship identification process. Particularly it is proposed to utilize as many non-redundant sources of information as possible that address specific vessel attributes. Another important recommendation states that the information fusion and data association techniques should be capable of dealing with incomplete and imprecise information. Some fuzzy logic techniques capable of tolerating imprecise and dissimilar data are proposed.

Development of an Information Fusion System for Engine Diagnostics and Health Management

NASA Technical Reports Server (NTRS)

Volponi, Allan J.; Brotherton, Tom; Luppold, Robert; Simon, Donald L.

2004-01-01

Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.

Biosensing of BCR/ABL fusion gene using an intensity-interrogation surface plasmon resonance imaging system

NASA Astrophysics Data System (ADS)

Wu, Jiangling; Huang, Yu; Bian, Xintong; Li, DanDan; Cheng, Quan; Ding, Shijia

2016-10-01

In this work, a custom-made intensity-interrogation surface plasmon resonance imaging (SPRi) system has been developed to directly detect a specific sequence of BCR/ABL fusion gene in chronic myelogenous leukemia (CML). The variation in the reflected light intensity detected from the sensor chip composed of gold islands array is proportional to the change of refractive index due to the selective hybridization of surface-bound DNA probes with target ssDNA. SPRi measurements were performed with different concentrations of synthetic target DNA sequence. The calibration curve of synthetic target sequence shows a good relationship between the concentration of synthetic target and the change of reflected light intensity. The detection limit of this SPRi measurement could approach 10.29 nM. By comparing SPRi images, the target ssDNA and non-complementary DNA sequence are able to be distinguished. This SPRi system has been applied for assay of BCR/ABL fusion gene extracted from real samples. This nucleic acid-based SPRi biosensor therefore offers an alternative high-effective, high-throughput label-free tool for DNA detection in biomedical research and molecular diagnosis.

Joint sparsity based heterogeneous data-level fusion for target detection and estimation

NASA Astrophysics Data System (ADS)

Niu, Ruixin; Zulch, Peter; Distasio, Marcello; Blasch, Erik; Shen, Dan; Chen, Genshe

2017-05-01

Typical surveillance systems employ decision- or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal and incur information loss. In this paper, we investigate data-level heterogeneous sensor fusion. Since the sensors monitor the common targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the measurement domain has a low intrinsic dimensionality. For heterogeneous sensor data, we develop a joint-sparse data-level fusion (JSDLF) approach based on the emerging joint sparse signal recovery techniques by discretizing the target state space. This approach is applied to fuse signals from multiple distributed radio frequency (RF) signal sensors and a video camera for joint target detection and state estimation. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices. Furthermore, for a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with near real-time accurate target position and velocity estimates.

Materials handbook for fusion energy systems

NASA Astrophysics Data System (ADS)

Davis, J. W.; Marchbanks, M. F.

A materials data book for use in the design and analysis of components and systems in near term experimental and commercial reactor concepts has been created by the Office of Fusion Energy. The handbook is known as the Materials Handbook for Fusion Energy Systems (MHFES) and is available to all organizations actively involved in fusion related research or system designs. Distribution of the MHFES and its data pages is handled by the Hanford Engineering Development Laboratory (HEDL), while its direction and content is handled by McDonnell Douglas Astronautics Company — St. Louis (MDAC-STL). The MHFES differs from other handbooks in that its format is geared more to the designer and structural analyst than to the materials scientist or materials engineer. The format that is used organizes the handbook by subsystems or components rather than material. Within each subsystem is information pertaining to material selection, specific material properties, and comments or recommendations on treatment of data. Since its inception a little more than a year ago, over 80 copies have been distributed to over 28 organizations consisting of national laboratories, universities, and private industries.

Integrated Multi-Aperture Sensor and Navigation Fusion

DTIC Science & Technology

2010-02-01

Visio, Springer-Verlag Inc., New York, 2004. [3] R. G. Brown and P. Y. C. Hwang , Introduction to Random Signals and Applied Kalman Filtering, Third...formulate Kalman filter vision/inertial measurement observables for other images without the need to know (or measure) their feature ranges. As compared...Internal Data Fusion Multi-aperture/INS data fusion is formulated in the feature domain using the complementary Kalman filter methodology [3]. In this

Different source image fusion based on FPGA

NASA Astrophysics Data System (ADS)

Luo, Xiao; Piao, Yan

2016-03-01

The fusion technology of video image is to make the video obtained by different image sensors complementary to each other by some technical means, so as to obtain the video information which is rich in information and suitable for the human eye system. Infrared cameras in harsh environments such as when smoke, fog and low light situations penetrating power, but the ability to obtain the details of the image is poor, does not meet the human visual system. Single visible light imaging can be rich in detail, high resolution images and for the visual system, but the visible image easily affected by the external environment. Infrared image and visible image fusion process involved in the video image fusion algorithm complexity and high calculation capacity, have occupied more memory resources, high clock rate requirements, such as software, c ++, c, etc. to achieve more, but based on Hardware platform less. In this paper, based on the imaging characteristics of infrared images and visible light images, the software and hardware are combined to obtain the registration parameters through software matlab, and the gray level weighted average method is used to implement the hardware platform. Information fusion, and finally the fusion image can achieve the goal of effectively improving the acquisition of information to increase the amount of information in the image.

Multisource image fusion method using support value transform.

PubMed

Zheng, Sheng; Shi, Wen-Zhong; Liu, Jian; Zhu, Guang-Xi; Tian, Jin-Wen

2007-07-01

With the development of numerous imaging sensors, many images can be simultaneously pictured by various sensors. However, there are many scenarios where no one sensor can give the complete picture. Image fusion is an important approach to solve this problem and produces a single image which preserves all relevant information from a set of different sensors. In this paper, we proposed a new image fusion method using the support value transform, which uses the support value to represent the salient features of image. This is based on the fact that, in support vector machines (SVMs), the data with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the SVM model. The mapped least squares SVM (mapped LS-SVM) is used to efficiently compute the support values of image. The support value analysis is developed by using a series of multiscale support value filters, which are obtained by filling zeros in the basic support value filter deduced from the mapped LS-SVM to match the resolution of the desired level. Compared with the widely used image fusion methods, such as the Laplacian pyramid, discrete wavelet transform methods, the proposed method is an undecimated transform-based approach. The fusion experiments are undertaken on multisource images. The results demonstrate that the proposed approach is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes, such as quality of visual information (Q(AB/F)), the mutual information, etc.

Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications

NASA Astrophysics Data System (ADS)

Budzan, Sebastian; Kasprzyk, Jerzy

2016-02-01

The problem of obstacle detection and recognition or, generally, scene mapping is one of the most investigated problems in computer vision, especially in mobile applications. In this paper a fused optical system using depth information with color images gathered from the Microsoft Kinect sensor and 3D laser range scanner data is proposed for obstacle detection and ground estimation in real-time mobile systems. The algorithm consists of feature extraction in the laser range images, processing of the depth information from the Kinect sensor, fusion of the sensor information, and classification of the data into two separate categories: road and obstacle. Exemplary results are presented and it is shown that fusion of information gathered from different sources increases the effectiveness of the obstacle detection in different scenarios, and it can be used successfully for road surface mapping.

URREF Reliability Versus Credibility in Information Fusion

DTIC Science & Technology

2013-07-01

Fusion, Vol. 3, No. 2, December, 2008. [31] E. Blasch, J. Dezert, and P. Valin , “DSMT Applied to Seismic and Acoustic Sensor Fusion,” Proc. IEEE Nat...44] E. Blasch, P. Valin , E. Bossé, “Measures of Effectiveness for High- Level Fusion,” Int. Conference on Information Fusion, 2010. [45] X. Mei, H...and P. Valin , “Information Fusion Measures of Effectiveness (MOE) for Decision Support,” Proc. SPIE 8050, 2011. [49] Y. Zheng, W. Dong, and E

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

PubMed Central

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-01-01

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated