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Sample records for radar target classification

  1. Feature analysis for indoor radar target classification

    NASA Astrophysics Data System (ADS)

    Bufler, Travis D.; Narayanan, Ram M.

    2016-05-01

    This paper analyzes the spectral features from human beings and indoor clutter for building and tuning Support Vector Machines (SVMs) classifiers for the purpose of classifying stationary human targets. The spectral characteristics were obtained through simulations using Finite Difference Time Domain (FDTD) techniques where the radar cross section (RCS) of humans and indoor clutter objects were captured over a wide range of frequencies, polarizations, aspect angles, and materials. Additionally, experimental data was obtained using a vector network analyzer. Two different feature sets for class discrimination are used from the acquired target and clutter RCS spectral data sets. The first feature vectors consist of the raw spectral characteristics, while the second set of feature vectors are statistical features extracted over a set frequency interval. Utilizing variables of frequency and polarization, a SVM classifier can be trained to classify unknown targets as a human or clutter. Classification accuracy over 80% can be effectively achieved given appropriate features.

  2. Radar target identification using probabilistic classification vector machines

    NASA Astrophysics Data System (ADS)

    Jouny, I.

    2016-05-01

    Radar target identification using probabilistic vector machines is investigated and tested using real radar data collected in a compact range for commercial aircraft models. Unlike relevance vector machines (RVM) that utilize zero-mean Gaussian prior for every weight for both negative and positive classes and are thus vulnerable to questionable (deceptive) vectors, probabilistic vector machines [2], alternatively, use nonnegative priors for the positive class and vice versa. This paper compares the performance of these machines with other target identification tools, and highlights scenarios where classification via a probabilistic vector machine is more plausible. The problem addressed in this paper is a M-ary target classification problem and is implemented as a set of pairwise comparisons between all competing hypotheses.

  3. Radar target classification by natural resonances: System analysis

    NASA Astrophysics Data System (ADS)

    Reddy, Peter C.

    1990-09-01

    This thesis examines the system implementation considerations of a resonance based radar target classification system. The basis of the system is the aspect and excitation independent property of electromagnetic scattering from a conducting body. Such a system consists of two components: pole extraction and annihilation filtering. The algorithms investigated here for these purposes are the Cadzow-Solomon pole extraction algorithm and the K-Pulse annihilation filter. Additionally, an aspect-dependent annihilation filter based on an inverse autoregressive moving average (ARMA) model is introduced. The procedures are applied to noise polluted synthetic data, as well as scattering data collected for a thin-wire and silver coated 1/72 scale model aircraft.

  4. Radar target classification studies: Software development and documentation

    NASA Astrophysics Data System (ADS)

    Kamis, A.; Garber, F.; Walton, E.

    1985-09-01

    Three computer programs were developed to process and analyze calibrated radar returns. The first program, called DATABASE, was developed to create and manage a random accessed data base. The second program, called FTRAN DB, was developed to process horizontal and vertical polarizations radar returns into different formats (i.e., time domain, circular polarizations and polarization parameters). The third program, called RSSE, was developed to simulate a variety of radar systems and to evaluate their ability to identify radar returns. Complete computer listings are included in the appendix volumes.

  5. Analysis and exploitation of multipath ghosts in radar target image classification.

    PubMed

    Smith, Graeme E; Mobasseri, Bijan G

    2014-04-01

    An analysis of the relationship between multipath ghosts and the direct target image for radar imaging is presented. A multipath point spread function (PSF) is defined that allows for specular reflections in the local environment and can allow the ghost images to be localized. Analysis of the multipath PSF shows that certain ghosts can only be focused for the far field synthetic aperture radar case and not the full array case. Importantly, the ghosts are shown to be equivalent to direct target images taken from different observation angles. This equivalence suggests that exploiting the ghosts would improve target classification performance, and this improvement is demonstrated using experimental data and a naïve Bayesian classifer. The maximum performance gain achieved is 32%. PMID:24577193

  6. rScene: a revolutionary low-cost micro-radar for target classification and tracking

    NASA Astrophysics Data System (ADS)

    Plummer, Thomas J.; Porter, Rich; Raines, Robert

    2014-06-01

    A small form factor, low cost radar named rScene® has been designed by McQ Inc. for the unattended detection, classification, tracking, and speed estimation of people and vehicles. This article will describe recent performance enhancements added to rScene® and present results relative to detection range and false alarms. Additionally, a low power (<1W) processing scheme is described that allows the rScene® to be deployed for longer duration, while still detecting desired target scenarios. Using the rScene® to detect other targets of interest like boats over water will also be addressed. Lastly, the lack of performance degradation due to hiding the rScene® in various types of concealed scenarios like behind walls, doors, foliage and camouflage material will be addressed. rScene® provides a variety of options to integrate the device into both wired and wireless communication infrastructures. Based on its sophisticated signal processing algorithms to classify targets and reject clutter, it allows for operation in challenging urban environments in which traditional unattended ground sensor modalities are less effective.

  7. Model-based inversion algorithm for ground penetration radar signal processing with correlation for target classification

    NASA Astrophysics Data System (ADS)

    Patz, Mark David

    A non-intrusive buried object classifier for a ground penetrating radar (GPR) system is developed. Various GPR data sets and the implemented processing are described. A model based inversion algorithm that utilizes correlation methodology for target classification is introduced. Experimental data was collected with a continuous wave GPR. Synthetic data was generated with a newly developed software package that implements mathematical models to predict the electromagnetic returns from an underground object. Sample targets and geometries were chosen to produce nine configurations/scenarios for analysis. The real measurement sets for each configuration and the synthetic sets for a family of similar configurations were imaged with the same state-of-the-art signal processing algorithms. The imaged results for the real data measurements were correlated with the imaged results for the synthetic data sets to produce performance measurements, thus producing a procedure that provides a non-invasive assessment of the object and medium determined by the synthetic data set that maximally correlated with the real data return. Synthetic results and experiment results showed good correlations. For the synthetic data, a mathematical model was developed for electromagnetic returns from an object shape (i.e., cylinder, parallelepiped, sphere) composed of a uniform construction (i.e., metal, wood, plastic, clay) within a uniform dielectric material (i.e., air, sand, loam, clay, water). This model was then implemented within a software package, thus providing the ability to generate simulated measurements from any combination of object, construction, and dielectric.

  8. Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network

    NASA Astrophysics Data System (ADS)

    Vasuki, Perumal; Roomi, S. Mohamed Mansoor

    2013-01-01

    Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.

  9. Supervised Classification of Natural Targets Using Millimeter-Wave Multifrequency Polarimetric Radar Measurements.

    NASA Astrophysics Data System (ADS)

    Lohmeier, Stephen Paul

    This dissertation classifies trees, snow, and clouds using multiparameter millimeter-wave radar data at 35, 95, and 225 GHz. Classification techniques explored include feedforward multilayer perceptron neural networks trained with standard backpropagation, Gaussian and minimum distance statistical classifiers, and rule-based classifiers. Radar data products, serving as features for classification, are defined, radar and in situ data are presented, scattering phenomenology is discussed, and the effect of data biases are analyzed. A neural network was able to discriminate between white pine trees and other broader-leaved trees with an accuracy of 97% using normalized Mueller matrix data at 225 GHz; wet, dry, melting, and freezing snow could be discriminated 89% of the time using 35, 95, and 225 GHz Mueller matrix data; and metamorphic and fresh snow could be differentiated 98% of the time using either the copolarized complex correlation coefficient or normalized radar cross section at three frequencies. A neural network was also able to discriminate ice clouds from water clouds using vertical and horizontal 95 GHz airborne reflectivity measurements with a success rate of 82% and 86% when viewing the clouds from the side and below respectively. Using 33 and 95 GHz data collected from the ground, a neural net was able to discriminate between ice clouds, liquid clouds, mixed phase clouds, rain, and insects 95% of the time using linear depolarization ratio, velocity, and range. As a precursor to this classification, a rule-based classifier was developed to label training pixels, since in situ data was not available for this particular data set. Attenuation biases in reflectivity were also removed with the aid of the rule-based classifier. A neural network using reflectivity in addition to other features was able to classify pixels correctly 96% of the time.

  10. Autonomous wireless radar sensor mote integrating a Doppler radar into a sensor mote and its application in surveillance and target material classification

    NASA Astrophysics Data System (ADS)

    Iftekharuddin, Khan; Khan, Muhammad M. R.; McCracken, Ernest; Wang, Lan; Kozma, Robert

    2011-09-01

    An autonomous wireless sensor network that consists of different types of sensor modalities is a topic of intense research due to its versatility and portability of applications. Typical autonomous sensor networks commonly include passive sensor nodes such as infrared, acoustic, seismic, and magnetic. However, fusion of another active sensor such as Doppler radar in the integrated sensor network may offer powerful capabilities for many different sensing and classification tasks. In this work, we demonstrate the design and implementation of an autonomous wireless sensor network integrating a Doppler sensor into wireless sensor node with commercial off the shelf components. Then we demonstrate two applications of the newly integrated radar mote in a wireless sensor network environment where other sensor motes are supporting the integrated radar mote for autonomous triggering and data collection. At first we use the integrated system to detect the range and velocity of a toy train effectively to demonstrate its capability as a surveillance tool. Then we classify different types of non-conducting target materials based on their reflected signal response to newly built radar mote. Different types of materials can usually affect the amount of energy reflected back to the source of an electromagnetic wave. For investigating this observation we simulate models for the reflectivity of different homogeneous non-conducting materials using a mathematical model and later classify the types of target materials using real experimental data collected through our autonomous radar-mote sensor network.

  11. Holographic neural networks versus conventional neural networks: a comparative evaluation for the classification of landmine targets in ground-penetrating radar images

    NASA Astrophysics Data System (ADS)

    Mudigonda, Naga R.; Kacelenga, Ray; Edwards, Mark

    2004-09-01

    This paper evaluates the performance of a holographic neural network in comparison with a conventional feedforward backpropagation neural network for the classification of landmine targets in ground penetrating radar images. The data used in the study was acquired from four different test sites using the landmine detection system developed by General Dynamics Canada Ltd., in collaboration with the Defense Research and Development Canada, Suffield. A set of seven features extracted for each detected alarm is used as stimulus inputs for the networks. The recall responses of the networks are then evaluated against the ground truth to declare true or false detections. The area computed under the receiver operating characteristic curve is used for comparative purposes. With a large dataset comprising of data from multiple sites, both the holographic and conventional networks showed comparable trends in recall accuracies with area values of 0.88 and 0.87, respectively. By using independent validation datasets, the holographic network"s generalization performance was observed to be better (mean area = 0.86) as compared to the conventional network (mean area = 0.82). Despite the widely publicized theoretical advantages of the holographic technology, use of more than the required number of cortical memory elements resulted in an over-fitting phenomenon of the holographic network.

  12. Polarization dependence in ultrawideband impulsive radar target versus clutter discrimination

    NASA Astrophysics Data System (ADS)

    Boerner, Wolfgang-Martin; Liu, Chuan-Li; Zhang, Xin; Naik, Vivek

    1992-05-01

    An account is given of the basic principles of radar polarimetry, and various optimization procedures for the propagation (scattering) range operator equation and the received-power expressions are presented and compared. On the basis of a complete description of isolated and distributed scatterers, polarimetric target classification, target vs clutter discrimination, and optimal polarimetric contrast enhancement algorithms are derived; these should be useful in the interpretation of wideband polarimetric data sets obtained with wideband coherent, dual (orthogonal) polarization channel radar systems.

  13. Extended Target Recognition in Cognitive Radar Networks

    PubMed Central

    Wei, Yimin; Meng, Huadong; Liu, Yimin; Wang, Xiqin

    2010-01-01

    We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches. PMID:22163464

  14. CASP9 Target Classification

    PubMed Central

    Kinch, Lisa N.; Shi, Shuoyong; Cheng, Hua; Cong, Qian; Pei, Jimin; Mariani, Valerio; Schwede, Torsten; Grishin, Nick V.

    2011-01-01

    The Critical Assessment of Protein Structure Prediction round 9 (CASP9) aimed to evaluate predictions for 129 experimentally determined protein structures. To assess tertiary structure predictions, these target structures were divided into domain-based evaluation units that were then classified into two assessment categories: template based modeling (TBM) and template free modeling (FM). CASP9 targets were split into domains of structurally compact evolutionary modules. For the targets with more than one defined domain, the decision to split structures into domains for evaluation was based on server performance. Target domains were categorized based on their evolutionary relatedness to existing templates as well as their difficulty levels indicated by server performance. Those target domains with sequence-related templates and high server prediction performance were classified as TMB, while those targets without identifiable templates and low server performance were classified as FM. However, using these generalizations for classification resulted in a blurred boundary between CASP9 assessment categories. Thus, the FM category included those domains without sequence detectable templates (25 target domains) as well as some domains with difficult to detect templates whose predictions were as poor as those without templates (5 target domains). Several interesting examples are discussed, including targets with sequence related templates that exhibit unusual structural differences, targets with homologous or analogous structure templates that are not detectable by sequence, and targets with new folds. PMID:21997778

  15. Classification of scattering objects from polarimetric radar images

    NASA Astrophysics Data System (ADS)

    Caillault, Sabine; Saillard, Joseph

    An automatic classification of geometrical targets is sought in order to simplify the interpretation which is necessary to read an image. The algorithms which have been developed are applied to real geometrical scattering objects measured during an X-pol radar campaign. Specific measurements and a precise analysis of this set of images provide the interpretation and the decomposition of many scattering effects. Several classification techniques are applied to the different parameters. One of the methods involved is a multidata analysis called PCA (principal components analysis). An algorithm of neural networks provides good results for the classification problem. Classification of geometrical scattering objects shows the interest of polarimetric parameters as well as the main advantages of neural networks for this particular application.

  16. Classification of radar clutter using neural networks.

    PubMed

    Haykin, S; Deng, C

    1991-01-01

    A classifier that incorporates both preprocessing and postprocessing procedures as well as a multilayer feedforward network (based on the back-propagation algorithm) in its design to distinguish between several major classes of radar returns including weather, birds, and aircraft is described. The classifier achieves an average classification accuracy of 89% on generalization for data collected during a single scan of the radar antenna. The procedures of feature selection for neural network training, the classifier design considerations, the learning algorithm development, the implementation, and the experimental results of the neural clutter classifier, which is simulated on a Warp systolic computer, are discussed. A comparative evaluation of the multilayer neural network with a traditional Bayes classifier is presented. PMID:18282874

  17. Computing the apparent centroid of radar targets

    SciTech Connect

    Lee, C.E.

    1996-12-31

    A high-frequency multibounce radar scattering code was used as a simulation platform for demonstrating an algorithm to compute the ARC of specific radar targets. To illustrate this simulation process, several targets models were used. Simulation results for a sphere model were used to determine the errors of approximation associated with the simulation; verifying the process. The severity of glint induced tracking errors was also illustrated using a model of an F-15 aircraft. It was shown, in a deterministic manner, that the ARC of a target can fall well outside its physical extent. Finally, the apparent radar centroid simulation based on a ray casting procedure is well suited for use on most massively parallel computing platforms and could lead to the development of a near real-time radar tracking simulation for applications such as endgame fuzing, survivability, and vulnerability analyses using specific radar targets and fuze algorithms.

  18. Classification of moving targets by a distributed sensor network

    NASA Astrophysics Data System (ADS)

    Khatri, Hiralal C.; Kirose, Getachew; Ranney, Kenneth; Innocenti, Roberto

    2004-09-01

    We present a procedure for classification of targets by a network of distributed radar sensors deployed to detect, locate and track moving targets. Estimated sensor positions and selected positions of a target under track are used to obtain the target aspect angle as seen by the sensors. This data is used to create a multi-angle profile of the target. Stored target templates are then matched in the least mean square sense with the target profile. These templates were generated from radar return signals collected from selected targets on a turntable. Probabilities of correct classification obtained by a simulation of the classification procedure are given as functions of signal-to-noise ratios and errors in estimates of target and sensor locations.

  19. Classification SAR targets with support vector machine

    NASA Astrophysics Data System (ADS)

    Cao, Lanying

    2007-02-01

    With the development of Synthetic Aperture Radar (SAR) technology, automatic target recognition (ATR) is becoming increasingly important. In this paper, we proposed a 3-class target classification system in SAR images. The system is based on invariant wavelet moments and support vector machine (SVM) algorithm. It is a two-stage approach. The first stage is to extract and select a small set of wavelet invariant moment features to indicate target images. The wavelet invariant moments take both advantages of the wavelet inherent property of multi-resolution analysis and moment invariants quality of invariant to translation, scaling changes and rotation. The second stage is classification of targets with SVM algorithm. SVM is based on the principle of structural risk minimization (SRM), which has been shown better than the principle of empirical risk minimization (ERM) which is used by many conventional networks. To test the performance and efficiency of the proposed method, we performed experiments on invariant wavelet moments, different kernel functions, 2-class identification, and 3-class identification. Test results show that wavelet invariant moments indicate the target effectively; linear kernel function achieves better results than other kernel functions, and SVM classification approach performs better than conventional nearest distance approach.

  20. Crop classification using airborne radar and LANDSAT data. [Colby, Kansas

    NASA Technical Reports Server (NTRS)

    Ulaby, F. T. (Principal Investigator); Li, R. Y.; Shanmugam, K. S.

    1981-01-01

    Airborne radar data acquired with a 13.3 GHz scatterometer over a test-site near Colby, Kansas were used to investigate the statistical properties of the scattering coefficient of three types of vegetation cover and of bare soil. A statistical model for radar data was developed that incorporates signal-fading and natural within-field variabilities. Estimates of the within-field and between-field coefficients of variation were obtained for each cover-type and compared with similar quantities derived from LANDSAT images of the same fields. The classification accuracy provided by LANDSAT alone, radar alone, and both sensors combined was investigated. The results indicate that the addition of radar to LANDSAT improves the classification accuracy by about 10; percentage-points when the classification is performed on a pixel basis and by about 15 points when performed on a field-average basis.

  1. Canonical Huynen decomposition of radar targets

    NASA Astrophysics Data System (ADS)

    Li, Dong; Zhang, Yunhua

    2015-10-01

    Huynen decomposition prefers the world of basic symmetry and regularity (SR) in which we live. However, this preference restricts its applicability to ideal SR scatterer only. As for the complex non-symmetric (NS) and irregular (IR) scatterers such as forest and building, Huynen decomposition fails to analyze their scattering. The canonical Huynen dichotomy is devised to extend Huynen decomposition to the preferences for IR and NS. From the physical realizability conditions of polarimetric scattering description, two other dichotomies of polarimetric radar target are developed, which prefer scattering IR, and NS, respectively, and provide two competent supplements to Huynen decomposition. The canonical Huynen dichotomy is the combination of the two dichotomies and Huynen decomposition. In virtue of an Adaptive selection, the canonical Huynen dichotomy is used in target extraction, and the experiments on AIRSAR San Francisco data demonstrate its high efficiency and excellent discrimination of radar targets.

  2. SVM based target classification using RCS feature vectors

    NASA Astrophysics Data System (ADS)

    Bufler, Travis D.; Narayanan, Ram M.; Dogaru, Traian

    2015-05-01

    This paper investigates the application of SVM (Support Vector Machines) for the classification of stationary human targets and indoor clutter via spectral features. Applying Finite Difference Time Domain (FDTD) techniques allows us to examine the radar cross section (RCS) of humans and indoor clutter objects by utilizing different types of computer models. FDTD allows for the spectral characteristics to be acquired over a wide range of frequencies, polarizations, aspect angles, and materials. The acquired target and clutter RCS spectral characteristics are then investigated in terms of their potential for target classification using SVMs. Based upon variables such as frequency and polarization, a SVM classifier can be trained to classify unknown targets as a human or clutter. Furthermore, the application of feature selection is applied to the spectral characteristics to determine the SVM classification accuracy of a reduced dataset. Classification accuracies of nearly 90% are achieved using radial and polynomial kernels.

  3. Unsupervised Classification of Global Radar Units on Venus

    NASA Technical Reports Server (NTRS)

    Kozak, R. C.; Davis, P. A.; Schaber, G. G.

    1985-01-01

    Characterization of the Venusian surface in terms of its radar properties was accomplished by application of an unsupervised, linear discriminant algorithm to two Pioneer-Venus (PV) Orbiter radar data sets: the RMS-slope (surface roughness) and reflectivity. Both databases were spatially filtered to the same effective resolution of 100 km prior to classification. A recent supervised classification study using these data was based on presupposed morphologic significance of selected data ranges. The knowledge of both Venusian geology and the geologic significance of the radar data is so limited that the data warrant a more unsupervised approach; for this study a linear discriminant classifier was chosen. This approach is purely statistical, thereby removing any observer bias. Statistical significance of the resulting clusters was evaluated by an ancillary program in which an F test utilizing the Mahalanobis' distance.

  4. Target & Propagation Models for the FINDER Radar

    NASA Technical Reports Server (NTRS)

    Cable, Vaughn; Lux, James; Haque, Salmon

    2013-01-01

    Finding persons still alive in piles of rubble following an earthquake, a severe storm, or other disaster is a difficult problem. JPL is currently developing a victim detection radar called FINDER (Finding Individuals in Emergency and Response). The subject of this paper is directed toward development of propagation & target models needed for simulation & testing of such a system. These models are both physical (real rubble piles) and numerical. Early results from the numerical modeling phase show spatial and temporal spreading characteristics when signals are passed through a randomly mixed rubble pile.

  5. Correlating Flight Behavior and Radar Measurements for Species Based Classification of Bird Radar Echoes for Wind Energy Site Assessment

    NASA Astrophysics Data System (ADS)

    Werth, S. P.; Frasier, S. J.

    2015-12-01

    Wind energy is one of the fastest-growing segments of the world energy market, offering a clean and abundant source of electricity. However, wind energy facilities can have detrimental effects on wildlife, especially birds and bats. Monitoring systems based on marine navigation radar are often used to quantify migration near potential wind sites, but the ability to reliably distinguish between bats and different varieties of birds has not been practically achieved. This classification capability would enable wind site selection that protects more vulnerable species, such as bats and raptors. Flight behavior, such as wing beat frequency, changes in speed, or changes in orientation, are known to vary by species [1]. The ability to extract these properties from radar data could ultimately enable a species based classification scheme. In this work, we analyze the relationship between radar measurements and bird flight behavior in echoes from avifauna. During the 2014 fall migration season, the UMass dual polarized weather radar was used to collect low elevation observations of migrating birds as they traversed through a fixed antenna beam. The radar was run during the night time, in clear-air conditions. Data was coherently integrated, and detections of biological targets exceeding an SNR threshold were extracted. Detections without some dominant frequency content (i.e. clear periodicity, potentially the wing beat frequency) were removed from the sample in order to isolate observations suspected to contain a single species or bird. For the remaining detections, measurements including the polarimetric products and the Doppler spectrum were extracted at each time step over the duration of the observation. The periodic and time changing nature of some of these different measurements was found to have a strong correlation with flight behavior (i.e. flapping vs. gliding behavior). Assumptions about flight behavior and orientation were corroborated through scattering

  6. A Human Gait Classification Method Based on Radar Doppler Spectrograms

    NASA Astrophysics Data System (ADS)

    Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam; Amin, Moeness G.

    2010-12-01

    An image classification technique, which has recently been introduced for visual pattern recognition, is successfully applied for human gait classification based on radar Doppler signatures depicted in the time-frequency domain. The proposed method has three processing stages. The first two stages are designed to extract Doppler features that can effectively characterize human motion based on the nature of arm swings, and the third stage performs classification. Three types of arm motion are considered: free-arm swings, one-arm confined swings, and no-arm swings. The last two arm motions can be indicative of a human carrying objects or a person in stressed situations. The paper discusses the different steps of the proposed method for extracting distinctive Doppler features and demonstrates their contributions to the final and desirable classification rates.

  7. A preliminary investigation of bird classification by Doppler radar

    NASA Technical Reports Server (NTRS)

    Martinson, L. W.

    1973-01-01

    A preliminary study of the application of Doppler radar to the classification of birds is reported. The desirability for improvements in bird classification stems primarily from the hazards they present to jet aircraft in flight and in the vicinity of airports. A secondary need exists in the study of bird migration. The wing body and tail motion of a bird in flight reflect signals which, when analyzed properly present a signature of wing beat pattern which is unique for each bird species. Although the results of this investigation did not validate the feasibility of classifying bird species, they do indicate that a more thorough investigation is warranted. Certain gross characteristics such as wing beat rates, multiple bird patterns, and bird maneuverability, were indicated clearly in the results. Large birds with slow wing beat rates appear to be the most optimum subject for further study with the X-band Doppler radar used in this investigation.

  8. Applications of neural networks to radar image classification

    SciTech Connect

    Hara, Yoshihisa; Atkins, R.G.; Yueh, S.H.; Shin, R.T.; Kong, J.A. )

    1994-01-01

    Classification of terrain cover using polarimetric radar is an area of considerable current interest and research. A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. Results show that one neural network method--Learning Vector Quantization (LVQ)--outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using Maximum Likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy. Performance after convergence is seen to be comparable to that obtained with a supervised ML classifier, while maintaining the advantages of an unsupervised technique.

  9. Synthetic aperture radar operator tactical target acquisition research

    NASA Technical Reports Server (NTRS)

    Hershberger, M. L.; Craig, D. W.

    1978-01-01

    A radar target acquisition research study was conducted to access the effects of two levels of 13 radar sensor, display, and mission parameters on operator tactical target acquisition. A saturated fractional-factorial screening design was employed to examine these parameters. Data analysis computed ETA squared values for main and second-order effects for the variables tested. Ranking of the research parameters in terms of importance to system design revealed four variables (radar coverage, radar resolution/multiple looks, display resolution, and display size) accounted for 50 percent of the target acquisition probability variance.

  10. Classification of radar data by detecting and identifying spatial and temporal anomalies

    NASA Astrophysics Data System (ADS)

    Väilä, Minna; Venäläinen, Ilkka; Jylhä, Juha; Ruotsalainen, Marja; Perälä, Henna; Visa, Ari

    2010-04-01

    For some time, applying the theory of pattern recognition and classification to radar signal processing has been a topic of interest in the field of remote sensing. Efficient operation and target indication is often hindered by the signal background, which can have similar properties with the interesting signal. Because noise and clutter may constitute most part of the response of surveillance radar, aircraft and other interesting targets can be seen as anomalies in the data. We propose an algorithm for detecting these anomalies on a heterogeneous clutter background in each range-Doppler cell, the basic unit in the radar data defined by the resolution in range, angle and Doppler. The analysis is based on the time history of the response in a cell and its correlation to the spatial surroundings. If the newest time window of response in a resolution cell differs statistically from the time history of the cell, the cell is determined anomalous. Normal cells are classified as noise or different type of clutter based on their strength on each Doppler band. Anomalous cells are analyzed using a longer time window, which emulates a longer coherent illumination. Based on the decorrelation behavior of the response in the long time window, the anomalous cells are classified as clutter, an airplane or a helicopter. The algorithm is tested with both experimental and simulated radar data. The experimental radar data has been recorded in a forested landscape.

  11. Wideband radar signal modeling of ground moving targets in clutter

    NASA Astrophysics Data System (ADS)

    Malas, John A.; Pasala, Krishna M.; Westerkamp, John J.

    2002-08-01

    Research in the area of air-to-ground target detection, track and identification (ID) requires the development of target signal models for known geometric shapes moving in ground clutter. Space-time adaptive filtering techniques in particular make good use of temporal-spatial synthetic radar signal return data. A radar signal model is developed to generate synthetic wideband radar signal data for use in multi-channel adaptive signal processing.

  12. Design of spectrally versatile forward-looking ground-penetrating radar for detection of concealed targets

    NASA Astrophysics Data System (ADS)

    Phelan, Brian R.; Ressler, Marc A.; Mazzaro, Gregory J.; Sherbondy, Kelly D.; Narayanan, Ram M.

    2013-05-01

    The design of high-resolution radars which can operate in theater involves a careful consideration of the radar's radiated spectrum. While a wide bandwidth yields better target detectability and classification, it can also interfere with other devices and/or violate federal and international communication laws. Under the Army Research Laboratory (ARL) Partnerships in Research Transition (PIRT) program, we are developing a Stepped-Frequency Radar (SFR) which allows for manipulation of the radiated spectrum, while still maintaining an effective ultra-wide bandwidth for achieving good range resolution. The SFR is a forward-looking, ultra-wideband (UWB) imaging radar capable of detecting concealed targets. This paper presents the research and analysis undertaken during the design of the SFR which will eventually complement an existing ARL system, the Synchronous Impulse REconstruction (SIRE) radar. The SFR is capable of excising prohibited frequency bands, while maintaining the down-range resolution capability of the original SIRE radar. The SFR has two transmit antennas and a 16-element receive antenna array, and this configuration achieves suitable cross-range resolution for target detection. The SFR, like the SIRE radar, is a vehicle mounted, forward-looking, ground penetrating radar (GPR) capable of using synthetic aperture radar (SAR) technology for the detection of subsurface targets via 3D imaging. Many contradicting design considerations are analyzed in this paper. The selection of system bandwidth, antenna types, number of antennas, frequency synthesizers, digitizers, receive amplifiers, wideband splitters, and many other components are critical to the design of the SFR. Leveraging commercial components and SIRE sub-systems were design factors offering an expedited time to the initial implementation of the radar while reducing overall costs. This SFR design will result in an ARL asset to support obscured target detection such as improvised explosive devices

  13. The Low Backscattering Targets Classification in Urban Areas

    NASA Astrophysics Data System (ADS)

    Shi, L.

    2012-07-01

    The Polarimetric and Interferometric Synthetic Aperture Radar (POLINSAR) is widely used in urban area nowadays. Because of the physical and geometric sensitivity, the POLINSAR is suitable for the city classification, power-lines detection, building extraction, etc. As the new X-band POLINSAR radar, the china prototype airborne system, XSAR works with high spatial resolution in azimuth (0.1 m) and slant range (0.4 m). In land applications, SAR image classification is a useful tool to distinguish the interesting area and obtain the target information. The bare soil, the cement road, the water and the building shadow are common scenes in the urban area. As it always exists low backscattering sign objects (LBO) with the similar scattering mechanism (all odd bounce except for shadow) in the XSAR images, classes are usually confused in Wishart-H-Alpha and Freeman-Durden methods. It is very hard to distinguish those targets only using the general information. To overcome the shortage, this paper explores an improved algorithm for LBO refined classification based on the Pre-Classification in urban areas. Firstly, the Pre-Classification is applied in the polarimetric datum and the mixture class is marked which contains LBO. Then, the polarimetric covariance matrix C3 is re-estimated on the Pre-Classification results to get more reliable results. Finally, the occurrence space which combining the entropy and the phase-diff standard deviation between HH and VV channel is used to refine the Pre-Classification results. The XSAR airborne experiments show the improved method is potential to distinguish the mixture classes in the low backscattering objects.

  14. Target discrimination in synthetic aperture radar using artificial neural networks.

    PubMed

    Principe, J C; Kim, M; Fisher, M

    1998-01-01

    This paper addresses target discrimination in synthetic aperture radar (SAR) imagery using linear and nonlinear adaptive networks. Neural networks are extensively used for pattern classification but here the goal is discrimination. We show that the two applications require different cost functions. We start by analyzing with a pattern recognition perspective the two-parameter constant false alarm rate (CFAR) detector which is widely utilized as a target detector in SAR. Then we generalize its principle to construct the quadratic gamma discriminator (QGD), a nonparametrically trained classifier based on local image intensity. The linear processing element of the QCD is further extended with nonlinearities yielding a multilayer perceptron (MLP) which we call the NL-QGD (nonlinear QGD). MLPs are normally trained based on the L(2) norm. We experimentally show that the L(2) norm is not recommended to train MLPs for discriminating targets in SAR. Inspired by the Neyman-Pearson criterion, we create a cost function based on a mixed norm to weight the false alarms and the missed detections differently. Mixed norms can easily be incorporated into the backpropagation algorithm, and lead to better performance. Several other norms (L(8), cross-entropy) are applied to train the NL-QGD and all outperformed the L(2) norm when validated by receiver operating characteristics (ROC) curves. The data sets are constructed from TABILS 24 ISAR targets embedded in 7 km(2) of SAR imagery (MIT/LL mission 90). PMID:18276330

  15. Tracking moving radar targets with parallel, velocity-tuned filters

    DOEpatents

    Bickel, Douglas L.; Harmony, David W.; Bielek, Timothy P.; Hollowell, Jeff A.; Murray, Margaret S.; Martinez, Ana

    2013-04-30

    Radar data associated with radar illumination of a movable target is processed to monitor motion of the target. A plurality of filter operations are performed in parallel on the radar data so that each filter operation produces target image information. The filter operations are defined to have respectively corresponding velocity ranges that differ from one another. The target image information produced by one of the filter operations represents the target more accurately than the target image information produced by the remainder of the filter operations when a current velocity of the target is within the velocity range associated with the one filter operation. In response to the current velocity of the target being within the velocity range associated with the one filter operation, motion of the target is tracked based on the target image information produced by the one filter operation.

  16. Waveform design for cognitive radar: target detection in heavy clutter

    NASA Astrophysics Data System (ADS)

    Kirk, Benjamin H.; Narayanan, Ram M.; Martone, Anthony F.; Sherbondy, Kelly D.

    2016-05-01

    In many applications of radar systems, detection of targets in environments with heavy clutter and interference can be difficult. It is desired that a radar system should detect targets at a further range as well as be able to detect these targets with very few false positive or negative readings. In a cognitive radar system, there are ways that these negative effects can be mitigated and target detection can be significantly improved. An important metric to focus on for increasing target detectability is the signal-to-clutter ratio (SCR). Cognitive radar offers solutions to issues such as this with the use of a priori knowledge of targets and environments as well as real time adaptations. A feature of cognitive radar that is of interest is the ability to adapt and optimize transmitted waveforms to a given situation. A database is used to hold a priori and dynamic knowledge of the operational environment and targets to be detected, such as clutter characteristics and target radar cross-section (RCS) estimations. Assuming this knowledge is available or can be estimated in real-time, the transmitted waveform can be tailored using methods such as transmission of a spectrum corresponding to the target-to-clutter ratio (TCR). These methods provide significant improvement in distinguishing targets from clutter or interference.

  17. Hydrometeor classification from polarimetric radar measurements: a clustering approach

    NASA Astrophysics Data System (ADS)

    Grazioli, Jacopo; Tuia, Devis; Berne, Alexis

    2015-04-01

    Hydrometeor classification is the process that aims at identifying the dominant type of hydrometeor (e.g. rain, hail, snow aggregates, hail, graupel, ice crystals) in a domain covered by a polarimetric weather radar during precipitation. The techniques documented in the literature are mostly based on numerical simulations and fuzzy logic. This involves the arbitrary selection of a set of hydrometeor classes and the numerical simulation of theoretical radar observations associated to each class. The information derived from the simulation is then applied to actual radar measurements by means of fuzzy logic input-output association. This approach has some limitations: the number and type of the hydrometeor categories undergoing identification is selected arbitrarily and the scattering simulations are based on constraining assumptions, especially in case of solid hydrometeors. Furthermore, in presence of noise and uncertainties, it is not guaranteed that the selected hydrometeor classes can be effectively identified in actual observations. In the present work we propose a different starting point for the classification task, which is based on observations instead of numerical simulations. We provide criteria for the selection of the number of hydrometeor classes that can be identified, by looking at how polarimetric observations collected over different precipitation events form clusters in the multi-dimensional space of the polarimetric variables. Two datasets, collected by an X-band weather radar, are employed in the study. The first dataset covers mountainous weather conditions (Swiss Alps), while the second includes Mediterranean orographic precipitation events collected during the special observation period (SOP) 2012 of the HyMeX campaign. We employ an unsupervised hierarchical clustering method to group the observations into clusters and we introduce a spatial smoothness constraint for the groups, assuming that the hydrometeor type changes smoothly in space

  18. Hydrometeor classification from polarimetric radar measurements: a clustering approach

    NASA Astrophysics Data System (ADS)

    Grazioli, J.; Tuia, D.; Berne, A.

    2015-01-01

    A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a priori, but they are learned from data. The approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (from which about 50 precipitation events are used in the present study). Seven hydrometeor classes (nopt = 7) have been found in the data set, and they have been identified as light rain (LR), rain (RN), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), and rimed-ice particles (RI).

  19. Hydrometeor classification from polarimetric radar measurements: a clustering approach

    NASA Astrophysics Data System (ADS)

    Grazioli, J.; Tuia, D.; Berne, A.

    2014-08-01

    A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number nopt of hydrometeor classes that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a-priori, but they are learned from data. The proposed approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (totalling about 3000 h of precipitation). Seven hydrometeor classes have been found in the data set and they have been associated to drizzle (DZ), light rain (LR), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), rimed particles (RI).

  20. Characteristics and optimization of radar target with plasma cover

    NASA Astrophysics Data System (ADS)

    Yang, Ying-ying; Zhao, Wei-fang; Wang, Wen-ting; Yi, Xiao-jing; Ji, Jun-wen; Lin, Xue-chun

    2013-09-01

    In this paper, we investigated the characteristic of radar target, the spherical and the pyramidal missile warheads, and compared the RCS and performance of the targets with and without the cover of the plasma metamaterials. Numerical simulation is obtained by the numerical calculation Finite-difference time-domain method (FDTD). The parameters of plasmonic structures as a metamaterial cloak was designed and optimized. The relationship between the parameters of the cloak and the corresponding electromagnetic characteristic of the target are analyzed by the simulation and discussion in broadband radar signals. After optimization, the plasma cover could attenuate 40 dBsm of the radar cross section (RCS) of the targets maximally. The result shows that the anomalous phenomenon of cloaking and stealth effects induced by plasma materials for the radar target, which might have potential application of military affairs.

  1. Automatic identification of bird targets with radar via patterns produced by wing flapping.

    PubMed

    Zaugg, Serge; Saporta, Gilbert; van Loon, Emiel; Schmaljohann, Heiko; Liechti, Felix

    2008-09-01

    Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research. PMID:18331979

  2. Classification and correction of the radar bright band with polarimetric radar

    NASA Astrophysics Data System (ADS)

    Hall, Will; Rico-Ramirez, Miguel; Kramer, Stefan

    2015-04-01

    The annular region of enhanced radar reflectivity, known as the Bright Band (BB), occurs when the radar beam intersects a layer of melting hydrometeors. Radar reflectivity is related to rainfall through a power law equation and so this enhanced region can lead to overestimations of rainfall by a factor of up to 5, so it is important to correct for this. The BB region can be identified by using several techniques including hydrometeor classification and freezing level forecasts from mesoscale meteorological models. Advances in dual-polarisation radar measurements and continued research in the field has led to increased accuracy in the ability to identify the melting snow region. A method proposed by Kitchen et al (1994), a form of which is currently used operationally in the UK, utilises idealised Vertical Profiles of Reflectivity (VPR) to correct for the BB enhancement. A simpler and more computationally efficient method involves the formation of an average VPR from multiple elevations for correction that can still cause a significant decrease in error (Vignal 2000). The purpose of this research is to evaluate a method that relies only on analysis of measurements from an operational C-band polarimetric radar without the need for computationally expensive models. Initial results show that LDR is a strong classifier of melting snow with a high Critical Success Index of 97% when compared to the other variables. An algorithm based on idealised VPRs resulted in the largest decrease in error when BB corrected scans are compared to rain gauges and to lower level scans with a reduction in RMSE of 61% for rain-rate measurements. References Kitchen, M., R. Brown, and A. G. Davies, 1994: Real-time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation. Q.J.R. Meteorol. Soc., 120, 1231-1254. Vignal, B. et al, 2000: Three methods to determine profiles of reflectivity from volumetric radar data to correct

  3. Moving target indicating radar applications in an integrated site security suite

    SciTech Connect

    Appenzeller, R.C. )

    1991-01-01

    The integration of a small, lightweight, low power consumption radar into a site security sensor suite can provide several key advantages in the ability to detect vehicles and personnel over large ground areas. This paper presents rationale for the inclusion of a man-portable Moving Target Indicator (MTI) radar in several security scenarios and outlines the technical specifics of a candidate radar. The Department of Energy (DOE) is currently investigating the effectiveness of a combination of optical sensors in concert with a scanning narrow beam radar at the Nevada Test Site in Mercury, Nevada. Demonstration results from these previous test activities are included herein. Of particular interest is the complimentary nature of this sensor suite where the large field of view achievable with radar allows the optical sensors to be used as pinpoint target classification devices. The inclusion of a radar minimizes operator fatigue caused by watching cameras scanning in azimuth and elevation. Advances in the areas of nuisance alarm rejection and improved range detection against single personnel targets were made in 1990 and this capability is included in the current production version.

  4. Radar cross sections of standard and complex shape targets

    NASA Technical Reports Server (NTRS)

    Sohel, M. S.

    1974-01-01

    The theoretical, analytical, and experimental results are described for radar cross sections (RCS) of different-shaped targets. Various techniques for predicting RCS are given, and RCS of finite standard targets are presented. Techniques used to predict the RCS of complex targets are made, and the RCS complex shapes are provided.

  5. Image simulation of geometric targets for synthetic aperture radar

    NASA Astrophysics Data System (ADS)

    Nasr, J. M.

    1989-10-01

    A new technique for image simulation which comes from a synthetic aperture radar is presented. The method is based on the embedding of an artificially simulated target in a real radar image captured by an operational antenna window on a satellite (SEASAT or SIR-B). A L and C band was used for the capture. The target dimensions studied were large enough for use with long waves provided the calculation techniques used with high frequencies were for an equivalent area radar (SER). The calculation of SER allows the capture of a raw signal received from the antennas. So that the possibility of simulation is low, some restrictions are made. The results are sufficiently interesting enough to let the study of the behavior of a particular target become of use to civilians or the military, in the functional bounds of radar waves.

  6. Hydrometeor classification using data mining techniques and polarimetric radar observations

    NASA Astrophysics Data System (ADS)

    Berne, A.; Grazioli, J.; Tuia, D.

    2013-12-01

    Hydrometeor classification aims at identifying the dominant type of hydrometeors in the sampling volume of a (polarimetric) weather radar. To do so, classical techniques make use of scattering simulations and fuzzy logic. A set of hydrometeor classes must be selected a-priori, and the scattering simulations are used to reproduce radar observations related with each class. Fuzzy logic is eventually used to link actually collected measurements with the simulated sets. With these methods, the number and type of hydrometeor categories undergoing identification is selected arbitrarily, the scattering simulations can be based on unreliable assumptions especially in case of solid particles and the effect of the noise on the measurements is not taken into account. In the present work, we develop a new approach to the classification problem, based on observations instead of scattering simulations. The goal is to provide objective criteria in the selection of the number of hydrometeor classes that can be reliably identified, by looking at how polarimetric observations collected over a set of different precipitation events form clusters in the multi-dimensional space of the polarimetric variables. Additional information is given by the spatial smoothness of the classified fields and by the altitude with respect to the zero degree level. Two polarimetric datasets, collected by an X-band radar are employed in this study. The two datasets cover weather conditions ranging from alpine precipitation collected in the Swiss Alps to Mediterranean orographic events, collected during the special observation period (SOP) 2012 of the HyMeX campaign. The optimal number of clusters is iteratively determined as a trade-off between the spatial smoothness of the classified domains and the complexity of the partitions , using an unsupervised clustering technique based on a correlation metrics. Eight clusters have been identified, 3 of them associated with liquid precipitation, 4 with solid

  7. Necessity to adapt land use and land cover classification systems to readily accept radar data

    NASA Technical Reports Server (NTRS)

    Drake, B.

    1977-01-01

    A hierarchial, four level, standardized system for classifying land use/land cover primarily from remote-sensor data (USGS system) is described. The USGS system was developed for nonmicrowave imaging sensors such as camera systems and line scanners. The USGS system is not compatible with the land use/land cover classifications at different levels that can be made from radar imagery, and particularly from synthetic-aperture radar (SAR) imagery. The use of radar imagery for classifying land use/land cover at different levels is discussed, and a possible revision of the USGS system to more readily accept land use/land cover classifications from radar imagery is proposed.

  8. Target Decomposition Techniques & Role of Classification Methods for Landcover Classification

    NASA Astrophysics Data System (ADS)

    Singh, Dharmendra; Mittal, Gunjan

    Target decomposition techniques aims at analyzing the received scattering matrix from polari-metric data to extract information about the scattering processes. Incoherent techniques have been modeled in recent years for providing more general approach for decomposition of natural targets. Therefore, there is a need to study and critically analyze the developing models for their suitability in classification of land covers. Moreover, the classification methods used for the segmentation of various landcovers from the decomposition techniques need to be examined as the appropriate selection of these methods affect the performance of the decomposition tech-niques for landcover classification. Therefore in the present paper, it is attempted to check the performance of various model based and an eigen vector based decomposition techniques for decomposition of Polarimetric PALSAR (Phased array type L band SAR) data. Few generic supervised classifiers were used for classification of decomposed images into three broad classes of water, urban and agriculture lands. For the purpose, algorithms had been applied twice on pre-processed PALSAR raw data once on spatial averaged (mean filtering on 33 window) data and the other on data, multilooked in azimuth direction by six looks and then filtered using Wishart Gamma MAP on 55 window. Classification of the decomposed images from each of the methods had been done using four supervised classifiers (parallelepiped, minimum distance, Mahalanobis and maximum likelihood). Ground truth data generated with the help of ground survey points, topographic sheet and google earth was used for the computation of classification accuracy. Parallelepiped classifier gave better classification accuracy of water class for all the models excluding H/A/Alpha. Minimum distance classifier gave better classification results for urban class. Maximum likelihood classifier performed well as compared to other classifiers for classification of vegetation class

  9. Image-based target detection with multispectral UWB OFDM radar

    NASA Astrophysics Data System (ADS)

    Bufler, Travis D.; Garmatyuk, Dmitriy S.

    2012-06-01

    This paper proposes an image-based automatic target detection algorithm to be used in clutter and sparse target environments. We intend to apply the algorithm to an ultra-wideband multispectral radar concept by means of employing multi-carrier waveforms based upon Orthogonal Frequency Division Multiplexing (OFDM) modulation. Individual sub-bands of an OFDM waveform can be processed separately to yield range and cross-range reconstruction of a target scene containing both targets and clutter. Target detection in resulting images will be performed and contrasted with the detection performance of a traditional fixed-waveform Synthetic Aperture Radar system. The target detection algorithm is implemented through the use of scalar and vector field operations performed on the images from the reconstructed target scene. We hypothesize that the use of vector operations and field analysis will allow for an adaptive approach to the detection of targets within clutter.

  10. Towards a theory of perception for radar targets

    NASA Astrophysics Data System (ADS)

    Huynen, J. R.

    An elimination of polarization bias can be achieved in radar detection if the target scattering matrix is known for the monostatic radar case. In the present treatment of methods for data representation of objects based on fields and on power, the polarized scattered return is in effect given a coherent wave field or completely polarized Stokes' vector power presentation; for targets, a presentation based on scattering matrix or Stokes matrix is given. A novel vector formulation is presented which relates to the cognitive requirement for a string of target features. The concept formulation of general object structures is shown to be diagramatically related to hierarchical object tree structures widely employed in AI.

  11. Automated target recognition using passive radar and coordinated flight models

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.; Lanterman, Aaron D.

    2003-09-01

    Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are particularly attractive since they allow receivers to operate without emitting energy, rendering them covert. Many existing passive radar systems estimate the locations and velocities of targets. This paper focuses on adding an automatic target recognition (ATR) component to such systems. Our approach to ATR compares the Radar Cross Section (RCS) of targets detected by a passive radar system to the simulated RCS of known targets. To make the comparison as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. The estimated positions become inputs for an algorithm that uses a coordinated flight model to compute probable aircraft orientation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of several potential target classes as they execute the estimated maneuvers. The RCS is then scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. The Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, so that the RCS can be further scaled. The Rician model compares the RCS of the illuminated aircraft with those of the potential targets. This comparison results in target identification.

  12. Titan's Lake Distribution and Classification from the Cassini RADAR

    NASA Astrophysics Data System (ADS)

    Aharonson, O.; Hayes, A.; Lewis, K.; Lunine, J.; Lorenz, R.; Mitchell, K.; Jannsen, M.; Mitri, G.; Wall, S. D.; Elachi, C.; Cassini RADAR Team

    2007-12-01

    A picture is emerging of surface hydrology in the north polar region of Titan. Quasi-circular, lobate to complex features, which take up 2.4% of the global coverage area (22.4% of the surface), are separated into 3 classes: dark lakes, granular lakes, and bright lakes. Dark lakes are interpreted as liquid filled while bright lakes are interpreted to be empty basins. Based on observed backscatter and geospatial position, granular lakes are inferred as transitional between dark and bright counterparts. In this work, the differences in distribution, morphology, and radiometric properties between the classes are explored using the Cassini Radar. The differences and similarities between the classes have implications for the interaction and evolution of hydrologic features on Titan. Dark lakes, which represent 84% of the mapped features, are found between 65°N and 90°N, and show a general trend of decreased off-nadir backscatter poleward. Granular lakes, which are distinguished from dark lakes by a higher backscatter cross-section relative to their surroundings, are found as low as 55°N and extend to 77°N. We have found no abrupt statistical change between dark and granular lakes, suggesting a smooth transition between the two classifications. Bright lakes, distinguished by their higher backscatter relative to their surroundings, represent ~10% of observed lakes. They are found in the same latitude range as granular lakes, often interspersed among them. Shoreline complexity, expressed as the fractal dimension, shows that bright and granular lakes are characteristically more circular than dark lakes. High resolution (~5 km) altimetry collected coincident with Synthetic Aperture Radar (SAR) images shows that bright lakes are empty basins 250-350 m in depth. Comparison between SAR images and integrated power in the altimetry waveforms shows that bright lakes have high return in both nadir and off-nadir backscatter relative to their surroundings. This allows

  13. Computer-aided methods of the LPI radar signal detection and classification

    NASA Astrophysics Data System (ADS)

    Grishin, Yury; Janczak, Dariusz

    2008-01-01

    The paper describes a possible structure of the LPI radar signal classification algorithm based on using a computer system with elements of the artificial intelligence (AI). Such an algorithm uses a combination of different signal processing tools such as the Wigner-Ville Distribution, the Wavelet Transform and the Cyclostationary Signal Analysis. The efficiency of these transformations with respect to different kinds of digital LPI radar signal modulation is considered. For a final classification and parameters extraction on the base of time-frequency or bifrequency representation the artificial intelligence methods can be used. One of the possible approaches to solving the radar signal classification problem is to use a proposed in the paper algorithm which consists of several steps: time-frequency or bifrequency transformations, a noise reduction procedure with using a two-dimensional filter, the RBF artificial neural network (NN) probability density function estimator which extracts the feature vector used for the final radar signal classification without an operator.

  14. Unsupervised classification of scattering behavior using radar polarimetry data

    NASA Technical Reports Server (NTRS)

    Van Zyl, Jakob J.

    1989-01-01

    The use of an imaging radar polarimeter data for unsupervised classification of scattering behavior is described by comparing the polarization properties of each pixel in a image to that of simple classes of scattering such as even number of reflections, odd number of reflections, and diffuse scattering. For example, when this algorithm is applied to data acquired over the San Francisco Bay area in California, it classifies scattering by the ocean as being similar to that predicted by the class of odd number of reflections, scattering by the urban area as being similar to that predicted by the class of even number of reflections, and scattering by the Golden Gate Park as being similar to that predicted by the diffuse scattering class. It also classifies the scattering by a lighthouse in the ocean and boats on the ocean surface as being similar to that predicted by the even number of reflections class, making it easy to identify these objects against the background of the surrounding ocean. The algorithm is also applied to forested areas and shows that scattering from clear-cut areas and agricultural fields is mostly similar to that predicted by the odd number of reflections class, while the scattering from tree-covered areas generally is classified as being a mixture of pixels exhibiting the characteristics of all three classes, although each pixel is identified with only a single class.

  15. An effective algorithm for radar dim moving target detection

    NASA Astrophysics Data System (ADS)

    Luo, Qian; Wang, Yanfei

    2009-10-01

    The detection and tracking of dim moving targets in very low signal-to-noise ratio (SNR) environment has been a difficult problem in radar signal processing. For low SNR moving targets detection, a new improved dynamic programming algorithm based on track-before-detection method is presented. This new algorithm integrates energy along target moving tracks according to target moving parameter information. This process substitutes the exhaustive search by a feasible algorithm. The simulation confirms that this algorithm, with high computational efficiency, is feasible, and can effectively estimate trajectories of dim closing moving targets. The process has also been shown to give an increase in detection.

  16. 5. Photocopy of photograph showing target tracking radar from 'Procedures ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    5. Photocopy of photograph showing target tracking radar from 'Procedures and Drills for the NIKE Hercules Missile Battery,' Department of the Army Field Manual, FM-44-82 from Institute for Military History, Carlisle Barracks, Carlisle, PA, 1959 - NIKE Missile Battery PR-79, East Windsor Road south of State Route 101, Foster, Providence County, RI

  17. Birds and insects as radar targets - A review

    NASA Technical Reports Server (NTRS)

    Vaughn, C. R.

    1985-01-01

    A review of radar cross-section measurements of birds and insects is presented. A brief discussion of some possible theoretical models is also given and comparisons made with the measurements. The comparisons suggest that most targets are, at present, better modeled by a prolate spheroid having a length-to-width ratio between 3 and 10 than by the often used equivalent weight water sphere. In addition, many targets observed with linear horizontal polarization have maximum cross sections much better estimated by a resonant half-wave dipole than by a water sphere. Also considered are birds and insects in the aggregate as a local radar 'clutter' source. Order-of-magnitude estimates are given for many reasonable target number densities. These estimates are then used to predict X-band volume reflectivities. Other topics that are of interest to the radar engineer are discussed, including the doppler bandwidth due to the internal motions of a single bird, the radar cross-section probability densities of single birds and insects, the variability of the functional form of the probability density functions, and the Fourier spectra of single birds and insects.

  18. Detection and identification of human targets in radar data

    NASA Astrophysics Data System (ADS)

    Gürbüz, Sevgi Z.; Melvin, William L.; Williams, Douglas B.

    2007-04-01

    Radar offers unique advantages over other sensors, such as visual or seismic sensors, for human target detection. Many situations, especially military applications, prevent the placement of video cameras or implantment seismic sensors in the area being observed, because of security or other threats. However, radar can operate far away from potential targets, and functions during daytime as well as nighttime, in virtually all weather conditions. In this paper, we examine the problem of human target detection and identification using single-channel, airborne, synthetic aperture radar (SAR). Human targets are differentiated from other detected slow-moving targets by analyzing the spectrogram of each potential target. Human spectrograms are unique, and can be used not just to identify targets as human, but also to determine features about the human target being observed, such as size, gender, action, and speed. A 12-point human model, together with kinematic equations of motion for each body part, is used to calculate the expected target return and spectrogram. A MATLAB simulation environment is developed including ground clutter, human and non-human targets for the testing of spectrogram-based detection and identification algorithms. Simulations show that spectrograms have some ability to detect and identify human targets in low noise. An example gender discrimination system correctly detected 83.97% of males and 91.11% of females. The problems and limitations of spectrogram-based methods in high clutter environments are discussed. The SNR loss inherent to spectrogram-based methods is quantified. An alternate detection and identification method that will be used as a basis for future work is proposed.

  19. Estimation of Radar Cross Section of a Target under Track

    NASA Astrophysics Data System (ADS)

    Jung, Young-Hun; Hong, Sun-Mog; Choi, Seung Ho

    2010-12-01

    In allocating radar beam for tracking a target, it is attempted to maintain the signal-to-noise ratio (SNR) of signal returning from the illuminated target close to an optimum value for efficient track updates. An estimate of the average radar cross section (RCS) of the target is required in order to adjust transmitted power based on the estimate such that a desired SNR can be realized. In this paper, a maximum-likelihood (ML) approach is presented for estimating the average RCS, and a numerical solution to the approach is proposed based on a generalized expectation maximization (GEM) algorithm. Estimation accuracy of the approach is compared to that of a previously reported procedure.

  20. Concealed target detection using augmented reality with SIRE radar

    NASA Astrophysics Data System (ADS)

    Saponaro, Philip; Kambhamettu, Chandra; Ranney, Kenneth; Sullivan, Anders

    2013-05-01

    The Synchronous Impulse Reconstruction (SIRE) forward-looking radar, developed by the U.S. Army Research Laboratory (ARL), can detect concealed targets using ultra-wideband synthetic aperture technology. The SIRE radar has been mounted on a Ford Expedition and combined with other sensors, including a pan/tilt/zoom camera, to test its capabilities of concealed target detection in a realistic environment. Augmented Reality (AR) can be used to combine the SIRE radar image with the live camera stream into one view, which provides the user with information that is quicker to assess and easier to understand than each separated. In this paper we present an AR system which utilizes a global positioning system (GPS) and inertial measurement unit (IMU) to overlay a SIRE radar image onto a live video stream. We describe a method for transforming 3D world points in the UTM coordinate system onto the video stream by calibrating for the intrinsic parameters of the camera. This calibration is performed offline to save computation time and achieve real time performance. Since the intrinsic parameters are affected by the zoom of the camera, we calibrate at eleven different zooms and interpolate. We show the results of a real time transformation of the SAR imagery onto the video stream. Finally, we quantify both the 2D error and 3D residue associated with our transformation and show that the amount of error is reasonable for our application.

  1. Active calibration target for bistatic radar cross-section measurements

    NASA Astrophysics Data System (ADS)

    Pienaar, M.; Odendaal, J. W.; Joubert, J.; Cilliers, J. E.; Smit, J. C.

    2016-05-01

    Either passive calibration targets are expensive and complex to manufacture or their bistatic radar cross section (RCS) levels are significantly lower than the monostatic RCS levels of targets such as spheres, dihedral, and trihedral corner reflectors. In this paper the performance of an active calibration target with relative high bistatic RCS values is illustrated as a reference target for bistatic RCS measurements. The reference target is simple to manufacture, operates over a wide frequency range, and can be configured to calibrate all four polarizations (VV, HH, HV, and VH). Bistatic RCS measurements of canonical targets, performed in a controlled environment, are calibrated with the reference target and the results are compared to simulated results using FEKO.

  2. External calibration of polarimetric radar images using distributed targets

    NASA Technical Reports Server (NTRS)

    Yueh, Simon H.; Nghiem, S. V.; Kwok, R.

    1992-01-01

    A new technique is presented for calibrating polarimetric synthetic aperture radar (SAR) images using only the responses from natural distributed targets. The model for polarimetric radars is assumed to be X = cRST where X is the measured scattering matrix corresponding to the target scattering matrix S distorted by the system matrices T and R (in general T does not equal R(sup t)). To allow for the polarimetric calibration using only distributed targets and corner reflectors, van Zyl assumed a reciprocal polarimetric radar model with T = R(sup t); when applied for JPL SAR data, a heuristic symmetrization procedure is used by POLCAL to compensate the phase difference between the measured HV and VH responses and then take the average of both. This heuristic approach causes some non-removable cross-polarization responses for corner reflectors, which can be avoided by a rigorous symmetrization method based on reciprocity. After the radar is made reciprocal, a new algorithm based on the responses from distributed targets with reflection symmetry is developed to estimate the cross-talk parameters. The new algorithm never experiences problems in convergence and is also found to converge faster than the existing routines implemented for POLCAL. When the new technique is implemented for the JPL polarimetric data, symmetrization and cross-talk removal are performed on a line-by-line (azimuth) basis. After the cross-talks are removed from the entire image, phase and amplitude calibrations are carried out by selecting distributed targets either with azimuthal symmetry along the looking direction or with some well-known volume and surface scattering mechanisms to estimate the relative phases and amplitude responses of the horizontal and vertical channels.

  3. Improving crop classification through attention to the timing of airborne radar acquisitions

    NASA Technical Reports Server (NTRS)

    Brisco, B.; Ulaby, F. T.; Protz, R.

    1984-01-01

    Radar remote sensors may provide valuable input to crop classification procedures because of (1) their independence of weather conditions and solar illumination, and (2) their ability to respond to differences in crop type. Manual classification of multidate synthetic aperture radar (SAR) imagery resulted in an overall accuracy of 83 percent for corn, forest, grain, and 'other' cover types. Forests and corn fields were identified with accuracies approaching or exceeding 90 percent. Grain fields and 'other' fields were often confused with each other, resulting in classification accuracies of 51 and 66 percent, respectively. The 83 percent correct classification represents a 10 percent improvement when compared to similar SAR data for the same area collected at alternate time periods in 1978. These results demonstrate that improvements in crop classification accuracy can be achieved with SAR data by synchronizing data collection times with crop growth stages in order to maximize differences in the geometric and dielectric properties of the cover types of interest.

  4. Gaussian process classification using automatic relevance determination for SAR target recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Xiangrong; Gou, Limin; Hou, Biao; Jiao, Licheng

    2010-10-01

    In this paper, a Synthetic Aperture Radar Automatic Target Recognition approach based on Gaussian process (GP) classification is proposed. It adopts kernel principal component analysis to extract sample features and implements target recognition by using GP classification with automatic relevance determination (ARD) function. Compared with k-Nearest Neighbor, Naïve Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that our algorithm is self-tuning and has better recognition accuracy as well.

  5. Passive synthetic aperture radar imaging of ground moving targets

    NASA Astrophysics Data System (ADS)

    Wacks, Steven; Yazici, Birsen

    2012-05-01

    In this paper we present a method for imaging ground moving targets using passive synthetic aperture radar. A passive radar imaging system uses small, mobile receivers that do not radiate any energy. For these reasons, passive imaging systems result in signicant cost, manufacturing, and stealth advantages. The received signals are obtained by multiple airborne receivers collecting scattered waves due to illuminating sources of opportunity such as commercial television, radio, and cell phone towers. We describe a novel forward model and a corresponding ltered-backprojection type image reconstruction method combined with entropy optimization. Our method determines the location and velocity of multiple targets moving at dierent velocities. Furthermore, it can accommodate arbitrary imaging geometries. we present numerical simulations to verify the imaging method.

  6. Characterizing targets and backgrounds for 3D laser radars

    NASA Astrophysics Data System (ADS)

    Steinvall, Ove K.; Larsson, Hakan; Gustafsson, Frank; Chevalier, Tomas R.; Persson, Asa; Klasen, Lena M.

    2004-12-01

    Exciting development is taking place in 3 D sensing laser radars. Scanning systems are well established for mapping from airborne and ground sensors. 3 D sensing focal plane arrays (FPAs) enable a full range and intensity image can be captured in one laser shot. Gated viewing systems also produces 3 D target information. Many applications for 3 D laser radars are found in robotics, rapid terrain visualization, augmented vision, reconnaissance and target recognition, weapon guidance including aim point selection and others. The net centric warfare will demand high resolution geo-data for a common description of the environment. At FOI we have a measurement program to collect data relevant for 3 D laser radars using airborne and tripod mounted equipment for data collection. Data collection spans from single pixel waveform collection (1 D) over 2 D using range gated imaging to full 3 D imaging using scanning systems. This paper will describe 3 D laser data from different campaigns with emphasis on range distribution and reflections properties for targets and background during different seasonal conditions. Example of the use of the data for system modeling, performance prediction and algorithm development will be given. Different metrics to characterize the data set will also be discussed.

  7. Radar 92; Proceedings of the International Conference, Brighton, United Kingdom, Oct. 12, 13, 1992

    NASA Astrophysics Data System (ADS)

    The present conference discusses topics indicative of the development status of radar simulation and modeling, sea and land clutter effects, multifunction and monopulse radar, radar propagation and target measurement, surveillance and tracking, clutter suppression, antenna designs, and air traffic control applications of radar systems. Also discussed are radar techniques for electronic warfare, antenna-related signal processing, SAR for remote sensing, multifunction signal processing, SAR and ISAR, radar target classification, bistatic radar, signal reconstruction, Doppler weather radar, and electronic warfare countermeasures.

  8. Synthetic aperture radar system design for random field classification

    NASA Technical Reports Server (NTRS)

    Harger, R. O.

    1973-01-01

    An optimum design study is carried out for synthetic aperture radar systems intended for classifying randomly reflecting areas (such as agricultural fields) characterized by a reflectivity density spectral density. The problem solution is obtained, neglecting interfield interference and assuming areas of known configuration and location, as well as a certain Gaussian signal field property. The optimum processor is nonlinear, but includes conventional matched filter processing. A set of summary design curves is plotted, and is applied to the design of a satellite synthetic aperture radar system.

  9. High-resolution radar ranging for multiple targets

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Willett, Peter K.; Bar-Shalom, Yaakov; Blair, W. Dale

    2002-08-01

    The similarity between the multiple-target radar ranging problem and the multi-user detection problem in CDMA is drawn: in CDMA, users' bits modulate distinct but correlated signature signals; while, in radar, the bits are range-bin occupancies and the signatures correspond to the known transmitted signal translated to be centered on the appropriate range bin. The analogy is useful: there has been a great deal of recent experience in CDMA, and one of the best and fastest algorithms uses a variant of probabilistic data association (PDA, the target-tracking philosophy). PDA can be augmented by group decision feedback (GDF) -- another idea from CDMA -- to refine the target delay estimates; and finally minimum description length (MDL) is applied to estimate the number of targets. Simulation examples are given to illustrate the resolution of closely spaced targets within what would normally be thought the same range bin. Its performance is also compared with the Cramer-Rao lower bound (CRLB) and the alternating projection (AP) algorithm.

  10. Target detection beneath foliage using polarimetric synthetic aperture radar interferometry

    NASA Astrophysics Data System (ADS)

    Cloude, S. R.; Corr, D. G.; Williams, M. L.

    2004-04-01

    In this paper, we demonstrate how the new technology of polarimetric synthetic aperture radar (SAR) interferometry can be used to enhance the detection of targets hidden beneath foliage. The key idea is to note that for random volume scattering, the interferometric coherence is invariant to changes in wave polarization. On the other hand, in the presence of a target the coherence changes with polarization. We show that under general symmetry constraints this change is linear in the complex coherence plane. These observations can be used to devise a filter to suppress the returns from foliage clutter while maintaining the signal from hidden targets. We illustrate the algorithm by applying it to coherent L-band SAR simulations of corner reflectors hidden in a forest. The simulations are performed using a voxel-based vector wave propagation and scattering code coupled to detailed structural models of tree architecture. In this way, the spatial statistics and radar signal fluctuations closely match those observed for natural terrain. We demonstrate significant improvements in the detection of hidden targets, which suggests that this technology has great potential for future foliage penetration (FOPEN) applications.

  11. Multi-agent system for target-adaptive radar tracking

    NASA Astrophysics Data System (ADS)

    O'Connor, Alan C.

    2012-06-01

    Sensor systems such as distributed sensor networks and radar systems are potentially agile - they have parameters that can be adjusted in real-time to improve the quality of data obtained for state-estimation and decision-making. The integration of such sensors with cyber systems involving many users or agents permits greater flexibility in choosing measurement actions. This paper considers the problem of selecting radar waveforms to minimize uncertainty about the state of a tracked target. Past work gave a tractable method for optimizing the choice of measurements when an accurate dynamical model is available. However, prior knowledge about a system is often not precise, for example, if the target under observation is an adversary. A multiple agent system is proposed to solve the problem in the case of uncertain target dynamics. Each agent has a different target model and the agents compete to explain past data and select the parameters of future measurements. Collaboration or competition between these agents determines which obtains access to the limited physical sensing resources. This interaction produces a self-aware sensor that adapts to changing information requirements.

  12. Moving target imaging using ultrawideband synthetic aperture radar

    NASA Astrophysics Data System (ADS)

    Guo, Hanwei; Liang, Diannong; Wan, Yan; Huang, Xiaotao; Dong, Zhen

    2003-09-01

    Moving Target High Resolution Imaging of Foliage Penetrate Ultra-Wide Band Synthetic Aperture Radar (FOPEN UWB SAR) is of great significance for battlefield awareness of concealed target. Great range migration and strong clutter make moving target detection and imaging difficult, especially the Signal to Clutter Ration(SCR) some times is so low that the moving targets is invisible in FOPEN UWB SAR imagery. To improve SCR, the clean technique is used in range compressed data domain. The clean technique and data reconstruction help single channel of FOPEN UWB SAR suppress strong tree clutter and stationary target signal from region of interest. A new definition called General Key-Stone Transform is given, which can correct any order of range migration. FOPEN UWB SAR has long integrated time. The plane and target moving in long time lead to complex range migration. To obtain high resolution imagery of moving target, General Key-Stone transform are applied to remove the range migration and realize multiple moving target data segment. Both General Key-Stone Transform and Clean Technique are applied in real data processing of FOPEN UWB SAR. The result shows that multiple moving targets in the trees are clearly detected and high resolution imagery is formed.

  13. Crop classification using multidate/multifrequency radar data. [Colby, Kansas

    NASA Technical Reports Server (NTRS)

    Ulaby, F. T. (Principal Investigator); Shanmugam, K. S.; Narayanan, V.; Dobson, C.

    1981-01-01

    Both C- and L-band radar data acquired over a test site near Colby, Kansas during the summer of 1978 were used to identify three types of vegetation cover and bare soil. The effects of frequency, polarization, and the look angle on the overall accuracy of recognizing the four types of ground cover were analyzed. In addition, multidate data were used to study the improvement in recognition accuracy possible with the addition of temporal information. The soil moisture conditions had changed considerably during the temporal sequence of the data; hence, the effects of soil moisture on the ability to discriminate between cover types were also analyzed. The results provide useful information needed for selecting the parameters of a radar system for monitoring crops.

  14. Improved accuracy of radar WPMM estimated rainfall upon application of objective classification criteria

    NASA Technical Reports Server (NTRS)

    Rosenfeld, Daniel; Amitai, Eyal; Wolff, David B.

    1995-01-01

    Application of the window probability matching method to radar and rain gauge data that have been objectively classified into different rain types resulted in distinctly different Z(sub e)-R relationships for the various classifications. These classification parameters, in addition to the range from the radar, are (a) the horizontal radial reflectivity gradients (dB/km); (b) the cloud depth, as scaled by the effective efficiency; (c) the brightband fraction within the radar field window; and (d) the height of the freezing level. Combining physical parameters to identify the type of precipitation and statistical relations most appropriate to the precipitation types results in considerable improvement of both point and areal rainfall measurements. A limiting factor in the assessment of the improved accuracy is the inherent variance between the true rain intensity at the radar measured volume and the rain intensity at the mouth of the rain guage. Therefore, a very dense rain gauge network is required to validate most of the suggested realized improvement. A rather small sample size is required to achieve a stable Z(sub e)-R relationship (standard deviation of 15% of R for a given Z(sub e)) -- about 200 mm of rainfall accumulated in all guages combined for each classification.

  15. Detection/tracking of moving targets with synthetic aperture radars

    NASA Astrophysics Data System (ADS)

    Newstadt, Gregory E.; Zelnio, Edmund; Gorham, Leroy; Hero, Alfred O., III

    2010-04-01

    In this work, the problem of detecting and tracking targets with synthetic aperture radars is considered. A novel approach in which prior knowledge on target motion is assumed to be known for small patches within the field of view. Probability densities are derived as priors on the moving target signature within backprojected SAR images, based on the work of Jao.1 Furthermore, detection and tracking algorithms are presented to take advantage of the derived prior densities. It was found that pure detection suffered from a high false alarm rate as the number of targets in the scene increased. Thus, tracking algorithms were implemented through a particle filter based on the Joint Multi-Target Probability Density (JMPD) particle filter2 and the unscented Kalman filter (UKF)3 that could be used in a track-before-detect scenario. It was found that the PF was superior than the UKF, and was able to track 5 targets at 0.1 second intervals with a tracking error of 0.20 +/- 1.61m (95% confidence interval).

  16. Scattering of Transient Electromagnetic Waves and Radar Target Discrimination.

    NASA Astrophysics Data System (ADS)

    Sun, Weimin

    A new scheme for radar target discrimination and identification, known as the Extinction-Pulse (E-pulse) technique, has been developed recently at Michigan State University. Some important characteristics and practical applications of this E-pulse technique are investigated in this thesis. The important characteristics investigated are the aspect-independency and the noise-insensitivity of the technique. The application of the technique to discriminate radar targets coated with lossy materials and the implementation of the technique using various antenna systems are also studied. The aspect-independency of the technique is attributed to the fact that the synthesis of the E-pulse waveform is based entirely on the natural frequencies of the target. The characteristic of noise-insensitivity is benefited from the convolutions of the E-pulse waveform with the scattering responses of the targets; an averaging and smoothing process. These two important characteristics have been experimentally investigated and are reported in this thesis. Recently it was found that the E-pulse technique can be applied to discriminate radar targets coated with a layer of lossy material. A theoretical study has been conducted to investigate the resonance modes of an infinitely long conducting cylinder coated with a layer of lossy material. The trajectory of the poles of resonance modes varying as a function of the parameters of the lossy coating and other geometrical factors is investigated. An experimental study was also conducted to discriminate various rectangular conducting plates coated with lossy materials. Since there is no existing method which can be used to predict accurately the natural frequencies of a thin rectangular plate, a new method based on a new set of coupled integral equations for the induced surface current, was developed. This set of integral equations is more rigorous and numerically better behaved when compared with some existing equations. These new integral

  17. Activity Monitoring and Motion Classification of the Lizard Chamaeleo jacksonii Using Multiple Doppler Radars

    PubMed Central

    Singh, Aditya; Lee, Scott SK; Butler, Marguerite; Lubecke, Victor

    2016-01-01

    We describe a simple, non-contact and efficient tool for monitoring the natural activity of a small lizard (Chamaeleo jacksonii) to yield valuable information about their metabolic activity and energy expenditure. It allows monitoring in a non-confined laboratory environment and uses multiple Doppler radars operating at 10.525 GHz. We developed a classification algorithm that can differentiate between fidgeting and locomotion by processing the quadrature baseband signals from the radars. The results have been verified by visual inspection and indicate that the tool could also be used for automated monitoring of the activities of reptiles and other small animals. PMID:23366934

  18. Ultrawideband radar target discrimination utilizing an advanced feature set

    NASA Astrophysics Data System (ADS)

    Nguyen, Lam H.; Kapoor, Ravinder; Wong, David C.; Sichina, Jeffrey

    1998-09-01

    The Army Research Laboratory, as part of its mission-funded applied research program, has been evaluating the utility of a low-frequency, ultra wideband imaging radar to detect tactical vehicles concealed by foliage. Measurement programs conducted at Aberdeen Proving Grounds and elsewhere have yielded a significant and unique database of extremely wideband and (in some cases) fully polarimetric data. Prior work has concentrated on developing computationally efficient methods to quickly canvass large quantities of data to identify likely target occurrences--often called `prescreening.' This paper reviews recent findings from our phenomenology/detection efforts. Included is a reformulated prescreener that has been trained and tested against a significantly larger data set than was used in the prior work. Also discussed are initial efforts aimed at the discrimination of targets from the difficult clutter remaining after prescreening. Performance assessments are included that detail detection rates versus false alarm levels.

  19. Synthetic aperture radar automatic target recognition using adaptive boosting

    NASA Astrophysics Data System (ADS)

    Sun, Yijun; Liu, Zhipeng; Todorovic, Sinisa; Li, Jian

    2005-05-01

    We propose a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the MSTAR public release database. First, each image chip is pre-processed by extracting fine and raw feature sets, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) net as the base learner. Since the RBF net is a binary classifier, we decompose our multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF net for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature.

  20. Detection of small, slow ground targets using Synthetic Aperture Radar

    NASA Technical Reports Server (NTRS)

    Chen, Curtis; Chapin, Elaine; Rosen, Paul

    2005-01-01

    Synthetic aperture radar (SAR) along-track interferometry (ATI) is a technique for sensing Earth-surface motion. The technique involves interferometrically combining data from two radar images acquired from phase centers separated along the platform flight track.

  1. Polarimetric Synthetic Aperture Radar data for Crop Cover Classification

    NASA Astrophysics Data System (ADS)

    Ramana, K. V.; Srikanth, P.; Deepika, U.; Sesha Sai, M. V. R.

    2014-11-01

    The interest in crop inventory through the use of microwave sensors is on the rise owing to need for accurate crop forecast and the availability of multi polarization data. Till recently, the temporal amplitude data has been used for crop discrimination as well as acreage estimation. With the availability of dual and quadpol data, the differential response of crop geometry at various crop growth stages to various polarizations is being exploited for discrimination and classification of crops. An attempt has been made in the current study with RISAT1 and Radarsat2 C-band single, dual, fully and hybrid polarimetric data for crop inventory. The single date hybrid polarimetric data gave comparable results to the three date single polarization data as well as with the single date fully polarimetric data for crops like rice and cotton.

  2. Modelling the performance of USV manoeuvring and target tracking: an approach using frequency modulated continuous wave radar rotary system.

    PubMed

    Onunka, Chiemela; Nnadozie, Remigius Chidozie

    2013-12-01

    The performance of frequency modulated continuous wave (FMCW) radar in tracking targets is presented and analysed. Obstacle detection, target tracking and radar target tracking performance models are developed and were used to investigate and to propose ways of improving the autonomous motion of unmanned surface vehicle (USV). Possible factors affecting the performance of FMCW radar in tracking targets are discussed and analysed. PMID:23853743

  3. Evaluation of second-order texture parameters for sea ice classification from radar images

    NASA Astrophysics Data System (ADS)

    Shokr, Mohammed E.

    1991-06-01

    With the advent of airborne and spaceborne synthetic aperture radar (SAR) systems, sea ice classification from SAR images has become an important research subject. Since gray tone alone has proven to be of limited capability in differentiating ice types, texture has naturally become an attractive avenue to explore. Accordingly, performance of texture quantification parameters as related to their ability to discriminate ice types has to be examined. SAR image appearance depends on radar parameters involved in the image construction procedures from the doppler history record. Therefore the feasibility of using universal texture/ice type relationships that hold for all combinations of radar parameters also has to be investigated. To that end, imagery data from three different SAR systems were used in this study. Five conventional texture parameters, derived from the gray level co-occurrence matrix (GLCM), were examined. Two of them were modified to ensure their invariant character under linear gray tone transformations. Results indicated that all parameters were highly correlated. The parameters did not, in general, vary with the computational variables used in generating co-occurrence matrices. Ice types can be identified uniquely by the mean value of any texture parameter. The relatively high variability of texture parameters, however, confuses ice discrimination, particularly of smoother ice types. Ice classification was conducted using a per-pixel maximum likelihood supervised scheme. When texture was combined with gray tone, the overall average classification accuracy was improved. Texture was successful in improving the classification accuracy of multiyear ice but was less promising in discriminating first-season ice types. The best two GLCM texture parameters, according to the computed overall average classification accuracies, were the inverse difference moment and the entropy. A brief description of GLCM texture parameters as related to ice's physical

  4. Development of a polarimetric radar based hydrometeor classification algorithm for winter precipitation

    NASA Astrophysics Data System (ADS)

    Thompson, Elizabeth Jennifer

    The nation-wide WSR-88D radar network is currently being upgraded for dual-polarized technology. While many convective, warm-season fuzzy-logic hydrometeor classification algorithms based on this new suite of radar variables and temperature have been refined, less progress has been made thus far in developing hydrometeor classification algorithms for winter precipitation. Unlike previous studies, the focus of this work is to exploit the discriminatory power of polarimetric variables to distinguish the most common precipitation types found in winter storms without the use of temperature as an additional variable. For the first time, detailed electromagnetic scattering of plates, dendrites, dry aggregated snowflakes, rain, freezing rain, and sleet are conducted at X-, C-, and S-band wavelengths. These physics-based results are used to determine the characteristic radar variable ranges associated with each precipitation type. A variable weighting system was also implemented in the algorithm's decision process to capitalize on the strengths of specific dual-polarimetric variables to discriminate between certain classes of hydrometeors, such as wet snow to indicate the melting layer. This algorithm was tested on observations during three different winter storms in Colorado and Oklahoma with the dual-wavelength X- and S-band CSU-CHILL, C-band OU-PRIME, and X-band CASA IP1 polarimetric radars. The algorithm showed success at all three frequencies, but was slightly more reliable at X-band because of the algorithm's strong dependence on KDP. While plates were rarely distinguished from dendrites, the latter were satisfactorily differentiated from dry aggregated snowflakes and wet snow. Sleet and freezing rain could not be distinguished from rain or light rain based on polarimetric variables alone. However, high-resolution radar observations illustrated the refreezing process of raindrops into ice pellets, which has been documented before but not yet

  5. Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

    NASA Technical Reports Server (NTRS)

    Rignot, E.; Chellappa, R.

    1993-01-01

    We present a maximum a posteriori (MAP) classifier for classifying multifrequency, multilook, single polarization SAR intensity data into regions or ensembles of pixels of homogeneous and similar radar backscatter characteristics. A model for the prior joint distribution of the multifrequency SAR intensity data is combined with a Markov random field for representing the interactions between region labels to obtain an expression for the posterior distribution of the region labels given the multifrequency SAR observations. The maximization of the posterior distribution yields Bayes's optimum region labeling or classification of the SAR data or its MAP estimate. The performance of the MAP classifier is evaluated by using computer-simulated multilook SAR intensity data as a function of the parameters in the classification process. Multilook SAR intensity data are shown to yield higher classification accuracies than one-look SAR complex amplitude data. The MAP classifier is extended to the case in which the radar backscatter from the remotely sensed surface varies within the SAR image because of incidence angle effects. The results obtained illustrate the practicality of the method for combining SAR intensity observations acquired at two different frequencies and for improving classification accuracy of SAR data.

  6. Localization of an air target by means of GNSS-based multistatic radar

    NASA Astrophysics Data System (ADS)

    Akhmedov, Daulet Sh.; Raskaliyev, Almat S.

    2016-08-01

    The possibility of utilizing transmitters of opportunity for target detection, tracking and positioning is of great interest to the radar community. In particular the optional use of Global Navigation Satellite System (GNSS) has lately triggered scientific research that has purpose to take advantage of this source of signal generation for passive radar. Number of studies have been conducted previously on development of GNSS-based bistatic and multistatic radars for detection and range estimation to the object located in the close atmosphere. To further enrich research in this area, we present a novel method for coordinate determination of the air target by means of the GNSS-based multistatic radar.

  7. Multiple target tracking and target attitude determination with a scanning laser radar

    NASA Technical Reports Server (NTRS)

    Flom, T.; Coombes, D.

    1974-01-01

    A scanning laser radar that can acquire and track single or multiple targets has recently been developed. Scan patterns have been designed for acquisition and tracking of one or more targets using a narrow laser beam. A synchronously scanned transmitter-receiver is used to acquire and track targets anywhere in a 376 x 376 element raster covering a 30 x 30 deg field. All scan patterns are electronically programmed, and the system automatically acquires and tracks the target or targets without the aid of an operator. The maximum tracking rate is 1.0 deg/sec (10.0 deg/sec) when used with a 1 kHz (10 kHz) scan rate. The estimated free space range against passive cooperative targets (corner cube reflectors) is 30 nautical miles. The laser radar has an accuracy of 10 cm (range) and 0.05 deg (angle). The developmental system is relatively small (1.5 cu ft), lightweight (60 lbs) and low-power-consuming (60 W).

  8. High range resolution radar target identification using the Prony model and hidden Markov models

    NASA Astrophysics Data System (ADS)

    Dewitt, Mark R.

    1992-12-01

    Fully polarized Xpatch signatures are transformed to two left circularly polarized signals. These two signals are then filtered by a linear FM pulse compression ('chirp') transfer function, corrupted by AWGN, and filtered by a filter matched to the 'chirp' transfer function. The bandwidth of the 'chirp' radar is about 750 MHz. Range profile feature extraction is performed using the TLS Prony Model parameter estimation technique developed at Ohio State University. Using the Prony Model, each scattering center is described by a polarization ellipse, relative energy, frequency response, and range. This representation of the target is vector quantized using a K-means clustering algorithm. Sequences of vector quantized scattering centers as well as sequences of vector quantized range profiles are used to synthesize target specific Hidden Markov Models (HMM's). The identification decision is made by determining which HMM has the highest probability of generating the unknown sequence. The data consist of synthesized Xpatch signatures of two targets which have been difficult to separate with other RTI algorithms. The RTI algorithm developed is clearly able to separate these two targets over a 10 by 10 degree (1 degree granularity) aspect angle window off the nose for SNR's as low as 0 dB. The classification rate is 100 percent for SNR's of 5 - 20 dB, 95 percent for a SNR of 0 dB and it drops rapidly for SNR's lower than 0 dB.

  9. Radar detection of low-altitude targets in a maritime environment. Volume 2: Meteorological and radar data

    NASA Astrophysics Data System (ADS)

    Anderson, Kenneth D.

    1993-11-01

    Results from a unique analytical and measurement effort to assess low-altitude short-range radar detection in an evaporation ducting environment validate propagation model predictions of reduced radar detection ranges within the radio horizon. Discrepancies between measured and predicted radar data demand a close examination of both meteorological data and surface layer theory. At ranges near and beyond the horizon, radar detection crucially depends both on the surface layer refractivity profile and on the adjacent mixed layer refractivity profile. An empirical model is described that merges the surface layer with the mixed layer forming a unified boundary layer. Other discrepancies, which are thought to be caused either by inadequate surface layer modeling (perhaps the moisture stability function) or by inadequate boundary layer meteorological measurements, suggest the need for improvements in surface layer modeling and new techniques to measure the refractivity structure. The combination of direct boundary layer (surface and mixed layer) meteorological measurements, remotely sensed radar measurements, and advanced numerical modeling capability provides valuable insight for a better understanding of the atmospheric boundary layer and its effects on the radar detection of low-altitude short-range targets.

  10. Marine Targets Classification in PolInSAR Data

    NASA Astrophysics Data System (ADS)

    Chen, Peng; Yang, Jingsong; Ren, Lin

    2014-11-01

    In this paper, marine stationary targets and moving targets are studied by Pol-In-SAR data of Radarsat-2. A new method of stationary targets detection is proposed. The method get the correlation coefficient image of the In-SAR data, and using the histogram of correlation coefficient image. Then, A Constant False Alarm Rate (CFAR) algorithm and The Probabilistic Neural Network model are imported to detect stationary targets. To find the moving targets, Azimuth Ambiguity is show as an important feature. We use the length of azimuth ambiguity to get the target's moving direction and speed. Make further efforts, Targets classification is studied by rebuild the surface elevation of marine targets.

  11. Synthetic aperture radar target detection, feature extraction, and image formation techniques

    NASA Technical Reports Server (NTRS)

    Li, Jian

    1994-01-01

    This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

  12. Neural networks for automated classification of ionospheric irregularities in HF radar backscattered signals

    NASA Astrophysics Data System (ADS)

    Wing, S.; Greenwald, R. A.; Meng, C.-I.; Sigillito, V. G.; Hutton, L. V.

    2003-08-01

    The classification of high frequency (HF) radar backscattered signals from the ionospheric irregularities (clutters) into those suitable, or not, for further analysis, is a time-consuming task even by experts in the field. We tested several different feedforward neural networks on this task, investigating the effects of network type (single layer versus multilayer) and number of hidden nodes upon performance. As expected, the multilayer feedforward networks (MLFNs) outperformed the single-layer networks. The MLFNs achieved performance levels of 100% correct on the training set and up to 98% correct on the testing set. Comparable figures for the single-layer networks were 94.5% and 92%, respectively. When measures of sensitivity, specificity, and proportion of variance accounted for by the model are considered, the superiority of the MLFNs over the single-layer networks is much more striking. Our results suggest that such neural networks could aid many HF radar operations such as frequency search, space weather, etc.

  13. Mean backscattering properties of random radar targets - A polarimetric covariance matrix concept

    NASA Astrophysics Data System (ADS)

    Ziegler, V.; Lueneburg, E.; Schroth, A.

    A polarimetric covariance matrix concept which describes the polarimetric backscattering features of reciprocal random radar targets is presented. The polarization dependence of second-order radar observables can be obtained by unitary similarity transformations of the covariance matrix. Invariant target parameters, such as the minimum and maximum eigenvalues or the eigenvalue difference of the covariance matrix, are introduced, providing information on the randomness of a target and the polarimetric features of the radar observables. An analytical formulation of the problem of optimal polarizations for the mean copolar and crosspolar power return is derived. As a result, the operational computation of optimal polarizations within large data sets becomes feasible.

  14. Polarimetric synthetic aperture radar image classification using fuzzy logic in the H/α-Wishart algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Teng; Yu, Jie; Li, Xiaojuan; Yang, Jie

    2015-01-01

    To solve the problem that the H/α-Wishart unsupervised classification algorithm can generate only inflexible clusters due to arbitrarily fixed zone boundaries in the clustering processing, a refined fuzzy logic based classification scheme called the H/α-Wishart fuzzy clustering algorithm is proposed in this paper. A fuzzy membership function was developed for the degree of pixels belonging to each class instead of an arbitrary boundary. To devise a unified fuzzy function, a normalized Wishart distance is proposed during the clustering step in the new algorithm. Then the degree of membership is computed to implement fuzzy clustering. After an iterative procedure, the algorithm yields a classification result. The new classification scheme is applied to two L-band polarimetric synthetic aperture radar (PolSAR) images and an X-band high-resolution PolSAR image of a field in LingShui, Hainan Province, China. Experimental results show that the classification precision of the refined algorithm is greater than that of the H/α-Wishart algorithm and that the refined algorithm performs well in differentiating shadows and water areas.

  15. Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

    NASA Astrophysics Data System (ADS)

    Jordan, Tyler S.

    2016-05-01

    This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

  16. Sparsity-based moving target localization using multiple dual-frequency radars under phase errors

    NASA Astrophysics Data System (ADS)

    Al Kadry, Khodour; Ahmad, Fauzia; Amin, Moeness G.

    2015-05-01

    In this paper, we consider moving target localization in urban environments using a multiplicity of dual-frequency radars. Dual-frequency radars offer the benefit of reduced complexity and fast computation time, thereby permitting real-time indoor target localization and tracking. The multiple radar units are deployed in a distributed system configuration, which provides robustness against target obscuration. We develop the dual-frequency signal model for the distributed radar system under phase errors and employ a joint sparse scene reconstruction and phase error correction technique to provide accurate target location and velocity estimates. Simulation results are provided that validate the performance of the proposed scheme under both full and reduced data volumes.

  17. Research on target recognition techniques of radar networking based on fuzzy mathematics

    NASA Astrophysics Data System (ADS)

    Guan, Chengbin; Wang, Guohong; Guan, Chengzhun; Pan, Jinshan

    2007-11-01

    Nowadays there are more and more targets, so it is more difficult for radar networking to track the important targets. To reduce the pressure on radar networking and the waste of ammunition, it is very necessary for radar networking to recognize the targets. Two target recognition approaches of radar networking based on fuzzy mathematics are proposed in this paper, which are multi-level fuzzy synthetical evaluation technique and lattice approaching degree technique. By analyzing the principles, the application techniques are given, the merits and shortcomings are also analyzed, and applying environments are advised. Another emphasis is the compare between the multiple mono-level fuzzy synthetical evaluation and the multi-level fuzzy synthetical evaluation, an instance is carried out to illuminate the problem, then the results are analyzed in theory, the conclusions are gotten which can be instructions for application in engineering.

  18. Electromagnetic modelling of Ground Penetrating Radar responses to complex targets

    NASA Astrophysics Data System (ADS)

    Pajewski, Lara; Giannopoulos, Antonis

    2014-05-01

    defined through a constant real value, or else its frequency-dispersion properties can be taken into account by incorporating into the model Debye approximations. The electromagnetic source can be represented as a simple line of current (in the case of two-dimensional models), a Hertzian dipole, a bow tie antenna, or else, the realistic description of a commercial antenna can be included in the model [2]. Preliminary results for some of the proposed cells are presented, obtained by using GprMax [3], a freeware tool which solves Maxwell's equations by using a second order in space and time Finite-Difference Time-Domain algorithm. B-Scans and A-Scans are calculated at 1.5 GHz, for the total electric field and for the field back-scattered by targets embedded in the cells. A detailed description of the structures, together with the relevant numerical results obtained to date, are available for the scientific community on the website of COST Action TU1208, www.GPRadar.eu. Research groups working on the development of electromagnetic forward- and inverse-scattering techniques, as well as on imaging methods, might test and compare the accuracy and applicability of their approaches on the proposed set of scenarios. The aim of this initiative is not that of identifying the best methods, but more properly to indicate the range of reliability of each approach, highlighting its advantages and drawbacks. In the future, the realisation of the proposed concrete cells and the acquisition of GPR experimental data would allow a very effective benchmark for forward and inverse scattering methods. References [1] R. Yelf, A. Ward, "Nine steps to concrete wisdom." Proc. 13th International Conference on Ground Penetrating Radar, Lecce, Italy, 21-25 June 2010, pp. 1-8. [2] C. Warren, A. Giannopoulos, "Creating FDTD models of commercial GPR antennas using Taguchi's optimisation method." Geophysics (2011), 76, article ID G37. [3] A. Giannopoulos, "Modelling ground penetrating radar by GPRMAX

  19. A system for the real-time display of radar and video images of targets

    NASA Technical Reports Server (NTRS)

    Allen, W. W.; Burnside, W. D.

    1990-01-01

    Described here is a software and hardware system for the real-time display of radar and video images for use in a measurement range. The main purpose is to give the reader a clear idea of the software and hardware design and its functions. This system is designed around a Tektronix XD88-30 graphics workstation, used to display radar images superimposed on video images of the actual target. The system's purpose is to provide a platform for tha analysis and documentation of radar images and their associated targets in a menu-driven, user oriented environment.

  20. System for Automatic Detection and Analysis of Targets in FMICW Radar Signal

    NASA Astrophysics Data System (ADS)

    Rejfek, Luboš; Mošna, Zbyšek; Urbář, Jaroslav; Koucká Knížová, Petra

    2016-01-01

    This paper presents the automatic system for the processing of the signals from the frequency modulated interrupted continuous wave (FMICW) radar and describes methods for the primary signal processing. Further, we present methods for the detection of the targets in strong noise. These methods are tested both on the real and simulated signals. The real signals were measured using the developed at the IAP CAS experimental prototype of FMICW radar with operational frequency 35.4 GHz. The measurement campaign took place at the TU Delft, the Netherlands. The obtained results were used for development of the system for the automatic detection and analysis of the targets measured by the FMICW radar.

  1. A model for forming airborne synthetic aperture radar images of underground targets

    SciTech Connect

    Doerry, A.W.

    1994-01-01

    Synthetic Aperture Radar (SAR) from an airborne platform has been proposed for imaging targets beneath the earth`s surface. The propagation of the radar`s energy within the ground, however, is much different than in the earth`s atmosphere. The result is signal refraction, echo delay, propagation losses, dispersion, and volumetric scattering. These all combine to make SAR image formation from an airborne platform much more challenging than a surface imaging counterpart. This report treats the ground as a lossy dispersive half-space, and presents a model for the radar echo based on measurable parameters. The model is then used to explore various imaging schemes, and image properties. Dynamic range is discussed, as is the impact of loss on dynamic range. Modified window functions are proposed to mitigate effects of sidelobes of shallow targets overwhelming deeper targets.

  2. Multiple-input Multiple-output Ground Moving Target Indicator Radar: Theory and Practice

    NASA Astrophysics Data System (ADS)

    Bliss, Dan

    2012-02-01

    Multiple-input multiple-output (MIMO) extensions to radar systems enable a number of advantages compared to traditional approaches. These advantages include improved angle estimation and target detection. In this paper, an overview of MIMO radar is provided, and the concept of coherent MIMO radar is defined. The principle focus of the paper is the discussion of MIMO ground moving target indication (GMTI). For GMTI radar modes, the advantages of a coherent MIMO architecture include improved angle estimation and enhanced slow speed target detection. To illustrate this, the concept of coherent MIMO radar is introduced and performance comparisons made between MIMO GMTI and traditional radar GMTI. These comparisons are supported by theoretical bounds, simulations, and experimental results for GMTI angle estimation accuracy and minimum detectable target velocity. For some applications, these results indicate significant potential improvements in clutter-mitigation, signal-to-noise ratio (SNR) loss, and reduction in angle-estimation error for slow-moving targets. The important effects of waveform characteristics is addressed.

  3. Detection, estimation, and discrimination of frequency diverse targets in ultra-wideband synthetic aperture radar data

    NASA Astrophysics Data System (ADS)

    Miller, Timothy R.

    New ultra-wideband radar imaging systems developed for ground-penetrating and foliage-penetrating applications are a departure from traditional, higher frequency radar systems. The assumption that targets are ideal point scatterers with impulsive responses is no longer acceptable. Target responses are frequency dependent and thus spread in time. The research outlined in this dissertation addresses target detection, estimation, and discrimination issues involved with processing frequency-dependent scattering returns. Frequency dependence is exploited in prescreening algorithms, new imaging algorithms and processing techniques to estimate time-domain target responses, and discrimination techniques based upon multiuser communications approaches. We present results and discuss the contributions of these studies.

  4. Target detection and identification using a stepped-frequency ultrawideband radar

    NASA Astrophysics Data System (ADS)

    Rothwell, Edward J.; Chen, Kun Mu; Nyquist, Dennis P.; Norman, Adam; Wallinga, G.; Dai, Y.

    1996-11-01

    Ultra-wideband radar systems provide great potential for radar target detection, identification and imaging through their inherent high-resolution capabilities. This paper considers two applications of a stepped-frequency ultra- wideband radar--detection of targets close to a disturbed sea surface, and imaging of airborne targets. A new technique for target detection is presented, based on the E- pulse concept and designed to eradicate the sea clutter signal while enhancing the target response. A simulation of a missile travelling above an evolving sea-water model is considered, and results are compared to measurements made in an anechoic chamber. Finally, the effects of signal bandwidth and bistatic angle on image resolution are explored, using a time-domain imaging identity with measured, band-limited signals.

  5. The effects of precipitation on radar target identification and imaging

    NASA Technical Reports Server (NTRS)

    Hodge, D. B.

    1975-01-01

    The properties of precipitation which will influence radar system design are discussed. The spatial characteristics of rainfall and the sizes and shapes of raindrops are described. The dielectric behavior of water is combined with these characteristics to determine the effects of rain on electromagnetic waves. These effects include: absorption, scatter, noise emission, phase shift, and depolarization.

  6. Improved superpixel-based polarimetric synthetic aperture radar image classification integrating color features

    NASA Astrophysics Data System (ADS)

    Xing, Yanxiao; Zhang, Yi; Li, Ning; Wang, Robert; Hu, Guixiang

    2016-04-01

    Various polarimetric features including scattering matrix, covariance matrix, polarimetric decomposition results, and textural or spatial information have already been used for polarimetric synthetic aperture radar (PolSAR) image classification. However, color features are rarely involved. We propose an improved superpixel-based PolSAR image classification integrating color features. First, we extract the color information using polarimetric decomposition. Second, by combining the color and spatial information of pixels, modified simple linear iterative clustering is used to generate small regions called superpixels. Then we apply Wishart distance to the superpixels to classify them into different classes. This method is demonstrated using the L-band Flevoland PolSAR data from AirSAR and Oberpfaffenhofen PolSAR data from ESAR. The results show that this method works well for areas with homogeneous terrains like farms in terms of both classification accuracy and computational efficiency. Furthermore, the success of the proposed method signifies that more color features can be discovered in the future research works.

  7. Classification on the monogenic scale space: application to target recognition in SAR image.

    PubMed

    Ganggang Dong; Gangyao Kuang

    2015-08-01

    This paper introduces a novel classification strategy based on the monogenic scale space for target recognition in Synthetic Aperture Radar (SAR) image. The proposed method exploits monogenic signal theory, a multidimensional generalization of the analytic signal, to capture the characteristics of SAR image, e.g., broad spectral information and simultaneous spatial localization. The components derived from the monogenic signal at different scales are then applied into a recently developed framework, sparse representation-based classification (SRC). Moreover, to deal with the data set, whose target classes are not linearly separable, the classification via kernel combination is proposed, where the multiple components of the monogenic signal are jointly considered into a unifying framework for target recognition. The novelty of this paper comes from: the development of monogenic feature via uniformly downsampling, normalization, and concatenation of the components at various scales; the development of score-level fusion for SRCs; and the development of composite kernel learning for classification. In particular, the comparative experimental studies under nonliteral operating conditions, e.g., structural modifications, random noise corruption, and variations in depression angle, are performed. The comparative experimental studies of various algorithms, including the linear support vector machine and the kernel version, the SRC and the variants, kernel SRC, kernel linear representation, and sparse representation of monogenic signal, are performed too. The feasibility of the proposed method has been successfully verified using Moving and Stationary Target Acquiration and Recognition database. The experimental results demonstrate that significant improvement for recognition accuracy can be achieved by the proposed method in comparison with the baseline algorithms. PMID:25872212

  8. Micromotion feature extraction of radar target using tracking pulses with adaptive pulse repetition frequency adjustment

    NASA Astrophysics Data System (ADS)

    Chen, Yijun; Zhang, Qun; Ma, Changzheng; Luo, Ying; Yeo, Tat Soon

    2014-01-01

    In multifunction phased array radar systems, different activities (e.g., tracking, searching, imaging, feature extraction, recognition, etc.) would need to be performed simultaneously. To relieve the conflict of the radar resource distribution, a micromotion feature extraction method using tracking pulses with adaptive pulse repetition frequencies (PRFs) is proposed in this paper. In this method, the idea of a varying PRF is utilized to solve the frequency-domain aliasing problem of the micro-Doppler signal. With appropriate atom set construction, the micromotion feature can be extracted and the image of the target can be obtained based on the Orthogonal Matching Pursuit algorithm. In our algorithm, the micromotion feature of a radar target is extracted from the tracking pulses and the quality of the constructed image is fed back into the radar system to adaptively adjust the PRF of the tracking pulses. Finally, simulation results illustrate the effectiveness of the proposed method.

  9. Sea clutter reduction and target enhancement by neural networks in a marine radar system.

    PubMed

    Vicen-Bueno, Raúl; Carrasco-Álvarez, Rubén; Rosa-Zurera, Manuel; Nieto-Borge, José Carlos

    2009-01-01

    The presence of sea clutter in marine radar signals is sometimes not desired. So, efficient radar signal processing techniques are needed to reduce it. In this way, nonlinear signal processing techniques based on neural networks (NNs) are used in the proposed clutter reduction system. The developed experiments show promising results characterized by different subjective (visual analysis of the processed radar images) and objective (clutter reduction, target enhancement and signal-to-clutter ratio improvement) criteria. Moreover, a deep study of the NN structure is done, where the low computational cost and the high processing speed of the proposed NN structure are emphasized. PMID:22573993

  10. A global search and rescue concept using synthetic aperture radar and passive user targets

    NASA Technical Reports Server (NTRS)

    Sivertson, W. E., Jr.

    1976-01-01

    A terrestrial search and rescue concept is defined embodying the use of passive radio-frequency reflectors in conjunction with an orbiting synthetic aperture radar to detect, identify, and locate users. An airborne radar test was conducted to evaluate the basic concept. In this test simple corner-reflector targets were successfully imaged. Results from this investigation were positive and indicate that the concept can be used to investigate new approaches focused on the development of a global search and rescue system.

  11. Signature predictions of surface targets undergoing turning maneuvers in spotlight synthetic aperture radar imagery

    NASA Astrophysics Data System (ADS)

    Garren, David A.

    2015-05-01

    This paper investigates methodologies for predicting the smear signatures in broadside spotlight synthetic aperture radar imagery collections due to surface targets that are undergoing turning maneuvers. This analysis examines the case of broadside geometry wherein the radar moves with constant speed and heading on a level flight path. This investigation concentrates moving target smear issues that yield some defocus in the range direction, although much smaller in magnitude than the motion induced smearing in the radar cross-range direction. This paper focuses on the case of a target that executes a turning maneuver during the SAR collection interval. The SAR simulations are shown to give excellent agreement between the moving target signatures and the predicted shapes of the central contours.

  12. Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis.

    PubMed

    Ritchie, Matthew; Ash, Matthew; Chen, Qingchao; Chetty, Kevin

    2016-01-01

    The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques. PMID:27589760

  13. Dual-polarization C-band weather radar algorithms for rain rate estimation and hydrometeor classification in an alpine region

    NASA Astrophysics Data System (ADS)

    Paulitsch, H.; Teschl, F.; Randeu, W. L.

    2009-03-01

    Dual polarization is becoming the standard for new weather radar systems. In contrast to conventional weather radars, where the reflectivity is measured in one polarization plane only, a dual polarization radar provides transmission in either horizontal, vertical, or both polarizations while receiving both the horizontal and vertical channels simultaneously. Since hydrometeors are often far from being spherical, the backscatter and propagation are different for horizontal and vertical polarization. Comparing the reflected horizontal and vertical power returns and their ratio and correlation, information on size, shape, and material density of cloud and precipitation particles can be obtained. The use of polarimetric radar variables can therefore increase the accuracy of the rain rate estimation compared to standard Z-R relationships of non-polarimetric radars. It is also possible to derive the type of precipitation from dual polarization parameters, although this is not an easy task, since there is no clear discrimination between the different values. Fuzzy logic approaches have been shown to work well with overlapping conditions and imprecisely defined class output. In this paper the implementation of different polarization algorithms for the new Austrian weather radar on Mt. Valluga is described, and first results from operational use are presented. This study also presents first observations of rain events in August 2007 during the test run of the radar. Further, the designated rain rate estimation and hydrometeor classification algorithms are explained.

  14. Random Noise Monopulse Radar System for Covert Tracking of Targets

    NASA Astrophysics Data System (ADS)

    Narayanan, Ram M.

    2002-07-01

    The University of Nebraska is currently developing a unique monopulse radar concept based on the use of random noise signal for covert tracking applications. This project is funded by the Missile Defense Agency (MDA). The advantage of this system over conventional frequency-modulated continuous wave (FMCW) or short pulse systems is its covertness resulting from the random waveform's immunity from interception and jamming. The system integrates a novel heterodyne correlation receiver with conventional monopulse architecture. Based on the previous work such as random noise interferometry, a series of theoretical analysis and simulations were conducted to examine the potential performance of this monopulse system. Furthermore, a prototype system is under development to exploit practical design aspects of phase comparison angle measurement. It is revealed that random noise monopulse radar can provide the same function as traditional monopulse radar, i.e., implement range and angular estimation and tracking in real time. The bandwidth of random noise signal can be optimized to achieve the best range resolution as well as the angular accuracy.

  15. Army Research Laboratory ultrawide-band testbed radar and comparisons of target data with models

    NASA Astrophysics Data System (ADS)

    Happ, Lynn; Ressler, Marc A.; Sturgess, Keith; Bennett, Matthew; Carin, Lawrence; Vitebskiey, S.

    1995-06-01

    Over the years, many different sensor types have been evaluated in an attempt to satisfy the need to detect and discriminate tactical and strategic targets concealed in foliage or underground. In large measure these early efforts were disappointing because of the lack of appropriate technologies. Today, by taking advantage of commercial off-the-shelf processors, an advanced analog-to-digital (A/D) converter, and lessons learned, a highly capable impulse radar has been designed and assembled to investigate an ultra-wideband (UWB) radar approach for ground penetration (GPEN) radar studies. The testbed consists of several major subsystems that are modular to allow for the evaluation of alternate approaches. The testbed radar subsystem consist of the antenna, the transmitter, the A/D converter, the processor/data storage system, the timing and control assembly, the positioning subsystem, and the operator interface computer. Many of the subassemblies exist as standard 19 inch rackmount units or as VME-compatible printed circuit assemblies. Much of the system operation is controlled by software, allowing easy modifications or other future upgrades. Data collected with this upgraded system will be used for measuring and analyzing the basic phenomenology of radar propagation through the ground and the response of targets, clutter, and targets embedded in clutter. One important aspect of basic phenomenology studies is validation of models with data. Range profiles of synthetic aperature radar (SAR) processed data from the Army Research Laboratory UWB radar is compared to 3D method of moments models for similar targets. In this paper, a mix of canonical and mine-like targets are examined and compared. Comparison between data and models shows some correlation, thus validating the need for further investigation.

  16. Ultrawideband radar echoes of land mine targets measured at oblique incidence using a 250-kW impulse radar system

    NASA Astrophysics Data System (ADS)

    Chant, Ian J.; Staines, Geoff

    1997-07-01

    United Nations Peacekeeping forces around the world need to transport food, personnel and medical supplies through disputed regions were land mines are in active use as road blocks and terror weapons. A method of fast, effective land mine detection is needed to combat this threat to road transport. The technique must operate from a vehicle travelling at a reasonable velocity and give warning far enough ahead for the vehicle to stop in time to avoid the land mine. There is particular interest in detecting low- metallic content land mines. One possible solutionis the use of ultra-wide-band (UWB) radar. The Australian Defence Department is investigating the feasibility of using UWB radar for land mine detection from a vehicle. A 3 GHz UWB system has been used to collect target response from a series of inert land mines and mine-like objects placed on the ground and buried in the ground. The targets measured were a subset of those in the target set described in Wong et al. with the addition of inert land mines corresponding to some of the surrogate targets in this set. The results are encouraging for the detection of metallic land mines and the larger non-metallic land mines. Smaller low-metallic- content anti-personnel land mines are less likely to be detected.

  17. SAR target classification based on multiscale sparse representation

    NASA Astrophysics Data System (ADS)

    Ruan, Huaiyu; Zhang, Rong; Li, Jingge; Zhan, Yibing

    2016-03-01

    We propose a novel multiscale sparse representation approach for SAR target classification. It firstly extracts the dense SIFT descriptors on multiple scales, then trains a global multiscale dictionary by sparse coding algorithm. After obtaining the sparse representation, the method applies spatial pyramid matching (SPM) and max pooling to summarize the features for each image. The proposed method can provide more information and descriptive ability than single-scale ones. Moreover, it costs less extra computation than existing multiscale methods which compute a dictionary for each scale. The MSTAR database and ship database collected from TerraSAR-X images are used in classification setup. Results show that the best overall classification rate of the proposed approach can achieve 98.83% on the MSTAR database and 92.67% on the TerraSAR-X ship database.

  18. Object-oriented classification using quasi-synchronous multispectral images (optical and radar) over agricultural surface

    NASA Astrophysics Data System (ADS)

    Marais Sicre, Claire; Baup, Frederic; Fieuzal, Remy

    2015-04-01

    In the context of climate change (with consequences on temperature and precipitation patterns), persons involved in agricultural management have the imperative to combine: sufficient productivity (as a response of the increment of the necessary foods) and durability of the resources (in order to restrain waste of water, fertilizer or environmental damages). To this end, a detailed knowledge of land use will improve the management of food and water, while preserving the ecosystems. Among the wide range of available monitoring tools, numerous studies demonstrated the interest of satellite images for agricultural mapping. Recently, the launch of several radar and optical sensors offer new perspectives for the multi-wavelength crop monitoring (Terrasar-X, Radarsat-2, Sentinel-1, Landsat-8…) allowing surface survey whatever the cloud conditions. Previous studies have demonstrated the interest of using multi-temporal approaches for crop classification, requiring several images for suitable classification results. Unfortunately, these approaches are limited (due to the satellite orbit cycle) and require waiting several days, week or month before offering an accurate land use map. The objective of this study is to compare the accuracy of object-oriented classification (random forest algorithm combined with vector layer coming from segmentation) to map winter crop (barley, rapeseed, grasslands and wheat) and soil states (bare soils with different surface roughness) using quasi-synchronous images. Satellite data are composed of multi-frequency and multi-polarization (HH, VV, HV and VH) images acquired near the 14th of April, 2010, over a studied area (90km²) located close to Toulouse in France. This is a region of alluvial plains and hills, which are mostly mixed farming and governed by a temperate climate. Remote sensing images are provided by Formosat-2 (04/18), Radarsat-2 (C-band, 04/15), Terrasar-X (X-band, 04/14) and ALOS (L-band, 04/14). Ground data are collected

  19. Target tracking using range-only measurements: application to ISAR mode of Ingara radar

    NASA Astrophysics Data System (ADS)

    Ristic, Branko; Arulampalam, Sanjeev; McCarthy, James

    2001-11-01

    The problem is on-line target state estimation from range and range-rate measurements. The motivation for this work comes from the need to track a target in the ISAR mode of the DSTO Ingara Multi-Mode Radar during an extended data collection. The paper makes three main contributions. First, the theoretical Cramér-Rao bound for the performance of an unbiased range-only tracking algorithm is derived. Second, three algorithms are developed and compared to the theoretical bounds of performance. Third, the developed techniques are applied to real data collected in the recent trials with the Ingara radar.

  20. Comparison of effects of sonar bandwidth for underwater target classification

    NASA Astrophysics Data System (ADS)

    Azimi-Sadjadi, Mahmood R.; Yao, De; Li, Donghui; Jamshidi, Arta A.; Dobeck, Gerald J.

    2000-08-01

    In this paper, two different data sets which use linear FM incident signals with different bandwidths, namely 40 KHz and 80 KHz, are used for benchmarking. The goal is to study the effects of using larger bandwidth for underwater target classification. The classification system is formed of several subsystems including preprocessing, a subband decomposition suing wavelet packets, linear predictive coding in subbands, feature selection and neural network classifier. The classification performance is demonstrated on ten noisy realizations of the data sets formed by adding synthesized reverberation effects with 12 dB signal-to- reverberation ratio. The ROC and the error location plots for these dat sets are generated. To compare the generalization and robustness of the system on these data sets, the error and classification rate statistics are generated using Monte Carlo simulations on a large set of noisy data. The results point to the fact that the wideband sonar provides better robustness property. Three-aspect fusion is also adopted which yields almost perfect classification performance. These issues will be thoroughly studied and analyzed in this paper.

  1. Radar remote sensing for crop classification and canopy condition assessment: Ground-data documentation

    NASA Technical Reports Server (NTRS)

    Ulaby, F. T. (Principal Investigator); Jung, B.; Gillespie, K.; Hemmat, M.; Aslam, A.; Brunfeldt, D.; Dobson, M. C.

    1983-01-01

    A vegetation and soil-moisture experiment was conducted in order to examine the microwave emission and backscattering from vegetation canopies and soils. The data-acquisition methodology used in conjunction with the mobile radar scatterometer (MRS) systems is described and associated ground-truth data are documented. Test fields were located in the Kansas River floodplain north of Lawrence, Kansas. Ten fields each of wheat, corn, and soybeans were monitored over the greater part of their growing seasons. The tabulated data summarize measurements made by the sensor systems and represent target characteristics. Target parameters describing the vegetation and soil characteristics include plant moisture, density, height, and growth stage, as well as soil moisture and soil-bulk density. Complete listings of pertinent crop-canopy and soil measurements are given.

  2. Radar and multispectral image fusion options for improved land cover classification

    NASA Astrophysics Data System (ADS)

    Villiger, Erwin J.

    Investigators engaged in research utilizing remotely-sensed data are increasingly faced with a plethora of data sources and platforms that exploit different portions of the electromagnetic spectrum. Considerable efforts have focused on the application of these sources to the development of a better understanding of lithosphere, biosphere, and atmospheric systems. Many of these efforts have concentrated on the use of single sensors. More recently, some research efforts have turned to the fusion of sources in an effort to determine if different sensors and platforms can be combined to more effectively analyze or model the systems in question. This study evaluates multisensor integration of Synthetic Aperture Radar (SAR) with Multispectral Imagery (MSI) data for land cover analysis and vegetation mapping. Three principle analytical issues are addressed in this investigation: the value of SAR collected at different incident angles, preclassification processing alternatives to improve fusion classification results, and the value of cross-season (dry and wet) data integration in a subtropical climate. The study site for this research is Andros Island, the largest island in The Bahamas archipelago. Andros has a number of distinct plant communities ranging from saltwater marsh and mangroves to pine stands and hardwood coppices. Despite the island's size and proximity to the United States, it is largely uninhabited and has large expanses of minimally disturbed landscapes. An empirical assessment of SAR filtering techniques, namely speckle suppression and texture analysis at various window sizes, is utilized to determine the most appropriate technique to apply when integrating SAR and MSI for land cover characterization. Multiple RADARSAT-1 SAR images were collected at various incident angles for wet and dry season conditions over the region of interest. Two Landsat Thematic Mapper-5 MSI datasets were also collected to coincide with the time periods of the SAR images. A land

  3. Multiple target three-dimensional coordinate estimation for bistatic MIMO radar with uniform linear receive array

    NASA Astrophysics Data System (ADS)

    Li, Jun; Li, Huan; Long, Libing; Liao, Guisheng; Griffiths, Hugh

    2013-12-01

    A novel scheme to achieve three-dimensional (3D) target location in bistatic radar systems is evaluated. The proposed scheme develops the additional information of the bistatic radar, that is the transmit angles, to estimate the 3D coordinates of the targets by using multiple-input multiple-output techniques with a uniform circular array on transmit and a uniform linear array on receive. The transmit azimuth, transmit elevation angles and receive cone angle of the targets are first extracted from the receive data and the 3D coordinates are then calculated on the basis of these angles. The geometric dilution of precision which is based on the root Cramer-Rao bound of the angles, is derived to evaluate the performance bound of the proposed scheme. Further, an ESPRIT based algorithm is developed to estimate the 3D coordinates of the targets. The advantages of this scheme are that the hardware of the receive array is reduced and the 3D coordinates of the targets can be estimated in the absence of the range information in bistatic radar. Simulations and analysis show that the proposed scheme has potential to achieve good performance with low-frequency radar.

  4. An optical radar for airborne use over natural waters. [for underwater target detection

    NASA Technical Reports Server (NTRS)

    Levis, C. A.; Swarner, W. G.; Prettyman, C.; Reinhardt, G. W.

    1975-01-01

    An optical radar for detecting targets in natural waters was built and tested in the Gulf of Mexico. The transmitter consists of a Q switched neodymium glass laser, with output amplified and doubled in KDP to 0.53 micrometer wavelength. The receiver incorporates a noval optical spatial filter to reduce the dynamic range required of the photodetector to a reasonable value. Detection of targets to a depth of 26 meters (84 feet) was achieved with a considerable sensitivity margin. The sensitivity of the radar is highly dependent on the optical attenuation coefficient. In general, measured returns fell between the values predicted on the basis of monopath and multipath attenuation. By means of simple physical arguments, a radar equation for the system was derived. To validate this theoretical model, measurements of optical attenuation and of water surface behavior were also instrumented, and some of these results are given.

  5. Radar detection of low-altitude targets in a maritime environment. Volume 1: Final analysis

    NASA Astrophysics Data System (ADS)

    Anderson, Kenneth D.

    1993-10-01

    Results from a unique analytical and measurement effort to assess low-altitude, short-range, radar detection capabilities in an evaporation ducting environment validate propagation model predictions of reduced radar detection ranges within the radio horizon. In addition, discrepancies between measured and predicted radar data demand a close examination of both meteorological data and surface layer theory. At ranges near and beyond the horizon, radar detection crucially depends both on the surface layer refractivity profile and on the adjacent mixed layer refractivity profile. A unified boundary layer model, an empirical model to merge the surface layer with the mixed layer, is described. Other discrepancies, which are thought to be caused either by inadequate surface layer modeling (perhaps the moisture stability function) or by inadequate boundary layer meteorological measurements, suggest the need for improvements in surface layer modeling and the need for new techniques to measure the refractivity structure. The combination of direct boundary layer (surface and mixed layer) meteorological measurements, remotely sensed radar measurements, and advanced numerical modeling capability provides valuable insight for a better understanding of the atmospheric boundary layer and its effects on the radar detection of low-altitude short-range targets.

  6. Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar.

    PubMed

    Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le

    2016-01-01

    Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar's estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method. PMID:27618058

  7. A modal radar cross section of thin-wire targets via the singularity expansion method

    NASA Technical Reports Server (NTRS)

    Richards, M. A.; Shumpert, T. H.; Riggs, L. S.

    1992-01-01

    A modal radar cross section (RCS) of arbitrary wire scatterers is constructed in terms of SEM parameters. Numerical results are presented for both straight and L-shaped wire targets and are compared to computations performed in the frequency domain using the method of moments.

  8. Non-Cooperative Target Imaging and Parameter Estimation with Narrowband Radar Echoes

    PubMed Central

    Yeh, Chun-mao; Zhou, Wei; Lu, Yao-bing; Yang, Jian

    2016-01-01

    This study focuses on the rotating target imaging and parameter estimation with narrowband radar echoes, which is essential for radar target recognition. First, a two-dimensional (2D) imaging model with narrowband echoes is established in this paper, and two images of the target are formed on the velocity-acceleration plane at two neighboring coherent processing intervals (CPIs). Then, the rotating velocity (RV) is proposed to be estimated by utilizing the relationship between the positions of the scattering centers among two images. Finally, the target image is rescaled to the range-cross-range plane with the estimated rotational parameter. The validity of the proposed approach is confirmed using numerical simulations. PMID:26805836

  9. Naval target classification by fusion of IR and EO sensors

    NASA Astrophysics Data System (ADS)

    Giompapa, S.; Croci, R.; Di Stefano, R.; Farina, A.; Gini, F.; Graziano, A.; Lapierre, F.

    2007-10-01

    This paper describes the classification function of naval targets performed by an infrared camera (IR) and an electro-optical camera (EO) that operate in a more complex multisensor system for the surveillance of a coastal region. The following naval targets are considered: high speed dinghy, motor boat, fishing boat, oil tanker. Target classification is automatically performed by exploiting the knowledge of the sensor confusion matrix (CM). The CM is analytically computed as a function of the sensor noise features, the sensor resolution, and the dimension of the involved image database. For both the sensors, a database of images is generated exploiting a three-dimensional (3D) Computer Aided Design (CAD) of the target, for the four types of ship mentioned above. For the EO camera, the image generation is simply obtained by the projection of the 3D CAD on the camera focal plane. For the IR images simulation, firstly the surface temperatures are computed using an Open-source Software for Modelling and Simulation of Infrared Signatures (OSMOSIS) that efficiently integrates the dependence of the emissivity upon the surface temperature, the wavelength, and the elevation angle. The software is applicable to realistic ship geometries. Secondly, these temperatures and the environment features are used to predict realistic IR images. The local decisions on the class are made using the elements of the confusion matrix of each sensor and they are fused according to a maximum likelihood (ML) rule. The global performance of the classification process is measured in terms of the global confusion matrix of the integrated system. This analytical approach can effectively reduce the computational load of a Monte Carlo simulation, when the sensors described here are introduced in a more complex multisensor system for the maritime surveillance.

  10. Classification of Traumatic Brain Injury for Targeted Therapies

    PubMed Central

    Saatman, Kathryn E.; Duhaime, Ann-Christine; Bullock, Ross; Maas, Andrew I.R.; Valadka, Alex

    2008-01-01

    Abstract The heterogeneity of traumatic brain injury (TBI) is considered one of the most significant barriers to finding effective therapeutic interventions. In October, 2007, the National Institute of Neurological Disorders and Stroke, with support from the Brain Injury Association of America, the Defense and Veterans Brain Injury Center, and the National Institute of Disability and Rehabilitation Research, convened a workshop to outline the steps needed to develop a reliable, efficient and valid classification system for TBI that could be used to link specific patterns of brain and neurovascular injury with appropriate therapeutic interventions. Currently, the Glasgow Coma Scale (GCS) is the primary selection criterion for inclusion in most TBI clinical trials. While the GCS is extremely useful in the clinical management and prognosis of TBI, it does not provide specific information about the pathophysiologic mechanisms which are responsible for neurological deficits and targeted by interventions. On the premise that brain injuries with similar pathoanatomic features are likely to share common pathophysiologic mechanisms, participants proposed that a new, multidimensional classification system should be developed for TBI clinical trials. It was agreed that preclinical models were vital in establishing pathophysiologic mechanisms relevant to specific pathoanatomic types of TBI and verifying that a given therapeutic approach improves outcome in these targeted TBI types. In a clinical trial, patients with the targeted pathoanatomic injury type would be selected using an initial diagnostic entry criterion, including their severity of injury. Coexisting brain injury types would be identified and multivariate prognostic modeling used for refinement of inclusion/exclusion criteria and patient stratification. Outcome assessment would utilize endpoints relevant to the targeted injury type. Advantages and disadvantages of currently available diagnostic, monitoring, and

  11. Performance limits for exo-clutter Ground Moving Target Indicator (GMTI) radar.

    SciTech Connect

    Doerry, Armin Walter

    2010-09-01

    The performance of a Ground Moving Target Indicator (GMTI) radar system depends on a variety of factors, many which are interdependent in some manner. It is often difficult to 'get your arms around' the problem of ascertaining achievable performance limits, and yet those limits exist and are dictated by physics. This report identifies and explores those limits, and how they depend on hardware system parameters and environmental conditions. Ultimately, this leads to a characterization of parameters that offer optimum performance for the overall GMTI radar system. While the information herein is not new to the literature, its collection into a single report hopes to offer some value in reducing the 'seek time'.

  12. An algorithm for automatic target recognition using passive radar and an EKF for estimating aircraft orientation

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.

    2005-07-01

    Rather than emitting pulses, passive radar systems rely on "illuminators of opportunity," such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern

  13. Deep transfer learning for automatic target classification: MWIR to LWIR

    NASA Astrophysics Data System (ADS)

    Ding, Zhengming; Nasrabadi, Nasser; Fu, Yun

    2016-05-01

    Publisher's Note: This paper, originally published on 5/12/2016, was replaced with a corrected/revised version on 5/18/2016. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. When dealing with sparse or no labeled data in the target domain, transfer learning shows its appealing performance by borrowing the supervised knowledge from external domains. Recently deep structure learning has been exploited in transfer learning due to its attractive power in extracting effective knowledge through multi-layer strategy, so that deep transfer learning is promising to address the cross-domain mismatch. In general, cross-domain disparity can be resulted from the difference between source and target distributions or different modalities, e.g., Midwave IR (MWIR) and Longwave IR (LWIR). In this paper, we propose a Weighted Deep Transfer Learning framework for automatic target classification through a task-driven fashion. Specifically, deep features and classifier parameters are obtained simultaneously for optimal classification performance. In this way, the proposed deep structures can extract more effective features with the guidance of the classifier performance; on the other hand, the classifier performance is further improved since it is optimized on more discriminative features. Furthermore, we build a weighted scheme to couple source and target output by assigning pseudo labels to target data, therefore we can transfer knowledge from source (i.e., MWIR) to target (i.e., LWIR). Experimental results on real databases demonstrate the superiority of the proposed algorithm by comparing with others.

  14. Numerical parametric study of buried target ground-penetrating radar signature

    NASA Astrophysics Data System (ADS)

    van den Bosch, Idesbald C.; Druyts, Pascal; Acheroy, Marc; Huynen, Isabelle

    2006-05-01

    The assessment of the performances of ground-penetrating radar (GPR) in humanitarian demining is an important problem. These performances are related to the relative strength of the target radar response with respect to that of the soil. Many parameters influence both responses. The physical and geometrical parameters that influence the target signature include the soil electromagnetic (EM) constitutive parameters, the target depth and orientation with respect to the soil surface, the antenna height and the target EM and geometrical properties. This work presents a numerical parametric study of the soil and target radar signatures. The advantages of the numerical approach are: it allows for a separate study of the influence of each parameters on the radar responses, it is fast, cheap, generic with regards to hardware, and finally it is not prone to experimental errors and hardware failures or misuse. Moreover it is always possible to link the numerical experiments with a particular hardware by characterizing this latter. However, a number of simplifications, such as modeling the soil as a planar multilayered medium, are introduced to keep the problem tractable. This study yields surprising results, such as for example the possibility of considering the target in homogeneous space for computing its signature, as soon as it is a few centimeters deep. The target considered in the numerical experiments is a dielectric cylinder representing an AP mine, with diameter 6 cm and height 5 cm, and ɛ rt=3. These values are chosen to approach as much as possible the physical properties of the M35BG AP mine, which is small and therefore difficult to detect.

  15. Sonar off-axis target classification by an echolocating dolphin

    NASA Astrophysics Data System (ADS)

    Moore, Patrick; Dankiewicz, Lois; Houser, Dorian

    2001-05-01

    Dolphin echolocation has evolved over millions of years under selection pressures imposed by a selective niche. The complexity and effectiveness of dolphin echolocation for detection and classification of objects within that niche has useful application to U. S. Naval objectives. In these environments, Navy dolphins are likely to first encounter targets on the edge of their sonar beam during a search. It is unknown, however, if target classification is possible from the off-axis (OA) information alone, or whether a more centrally focused interrogation is necessary. This talk addresses the initial findings of an animal detecting two different targets (cylinder and sphere) presented OA (left and ). Data collection methods will be presented. Outgoing echolocation clicks and echoes are digitized and stored to a PC for acoustic characterization using a high-speed Integrated Circuits Systems, Ltd. 32 channel A/D card, sampling 24 calibrated monitor hydrophones and analog filter-amplifiers arranged in a hemispherical support web in front of the animal. Emitted signals analyzed for various acoustic characteristics are discussed as well as detection performance. Since this is an on-going study, available results to date will be presented.

  16. Target-classification approach applied to active UXO sites

    NASA Astrophysics Data System (ADS)

    Shubitidze, F.; Fernández, J. P.; Shamatava, Irma; Barrowes, B. E.; O'Neill, K.

    2013-06-01

    This study is designed to illustrate the discrimination performance at two UXO active sites (Oklahoma's Fort Sill and the Massachusetts Military Reservation) of a set of advanced electromagnetic induction (EMI) inversion/discrimination models which include the orthonormalized volume magnetic source (ONVMS), joint diagonalization (JD), and differential evolution (DE) approaches and whose power and flexibility greatly exceed those of the simple dipole model. The Fort Sill site is highly contaminated by a mix of the following types of munitions: 37-mm target practice tracers, 60-mm illumination mortars, 75-mm and 4.5'' projectiles, 3.5'', 2.36'', and LAAW rockets, antitank mine fuzes with and without hex nuts, practice MK2 and M67 grenades, 2.5'' ballistic windshields, M2A1-mines with/without bases, M19-14 time fuzes, and 40-mm practice grenades with/without cartridges. The site at the MMR site contains targets of yet different sizes. In this work we apply our models to EMI data collected using the MetalMapper (MM) and 2 × 2 TEMTADS sensors. The data for each anomaly are inverted to extract estimates of the extrinsic and intrinsic parameters associated with each buried target. (The latter include the total volume magnetic source or NVMS, which relates to size, shape, and material properties; the former includes location, depth, and orientation). The estimated intrinsic parameters are then used for classification performed via library matching and the use of statistical classification algorithms; this process yielded prioritized dig-lists that were submitted to the Institute for Defense Analyses (IDA) for independent scoring. The models' classification performance is illustrated and assessed based on these independent evaluations.

  17. Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

    PubMed

    Na, X D; Zang, S Y; Wu, C S; Li, W L

    2015-11-01

    Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area. PMID

  18. Natural resonance extraction and annihilation filtering methods for radar target identification

    NASA Astrophysics Data System (ADS)

    Murphy, Timothy J.

    1990-09-01

    This thesis represents an initial attempt to demonstrate aspect independent target identification of complex radar targets using annihilation filters based on the natural resonances of the targets. The Cadzow-Solomon signal processing algorithm is tested to determine its suitability for the task of extracting the poles from complex targets to a degree of accuracy required for successful implementation of an annihilation filtering target identification system. This testing was conducted through the use of noise polluted synthetic data as well as measured transient scattering data from thin-wire and silver coated scale model aircraft targets. The testing revealed that the Cadzow-Solomon algorithm can return pole clusters at false pole locations when processing the scattered returns from complex targets. Properties of annihilation filters which may affect their ability to discriminate complex targets are examined.

  19. Benchmark radar targets for the validation of computational electromagnetics programs

    NASA Technical Reports Server (NTRS)

    Woo, Alex C.; Wang, Helen T. G.; Schuh, Michael J.; Sanders, Michael L.

    1993-01-01

    Results are presented of a set of computational electromagnetics validation measurements referring to three-dimensional perfectly conducting smooth targets, performed for the Electromagnetic Code Consortium. Plots are presented for both the low- and high-frequency measurements of the NASA almond, an ogive, a double ogive, a cone-sphere, and a cone-sphere with a gap.

  20. Prediction of buried mine-like target radar signatures using wideband electromagnetic modeling

    SciTech Connect

    Warrick, A.L.; Azevedo, S.G.; Mast, J.E.

    1998-04-06

    Current ground penetrating radars (GPR) have been tested for land mine detection, but they have generally been costly and have poor performance. Comprehensive modeling and experimentation must be done to predict the electromagnetic (EM) signatures of mines to access the effect of clutter on the EM signature of the mine, and to understand the merit and limitations of using radar for various mine detection scenarios. This modeling can provide a basis for advanced radar design and detection techniques leading to superior performance. Lawrence Livermore National Laboratory (LLNL) has developed a radar technology that when combined with comprehensive modeling and detection methodologies could be the basis of an advanced mine detection system. Micropower Impulse Radar (MIR) technology exhibits a combination of properties, including wideband operation, extremely low power consumption, extremely small size and low cost, array configurability, and noise encoded pulse generation. LLNL is in the process of developing an optimal processing algorithm to use with the MIR sensor. In this paper, we use classical numerical models to obtain the signature of mine-like targets and examine the effect of surface roughness on the reconstructed signals. These results are then qualitatively compared to experimental data.

  1. A novel data-driven learning method for radar target detection in nonstationary environments

    DOE PAGESBeta

    Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

    2016-04-12

    Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

  2. Acoustic target detection and classification using neural networks

    NASA Technical Reports Server (NTRS)

    Robertson, James A.; Conlon, Mark

    1993-01-01

    A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

  3. Antenna Allocation in MIMO Radar with Widely Separated Antennas for Multi-Target Detection

    PubMed Central

    Gao, Hao; Wang, Jian; Jiang, Chunxiao; Zhang, Xudong

    2014-01-01

    In this paper, we explore a new resource called multi-target diversity to optimize the performance of multiple input multiple output (MIMO) radar with widely separated antennas for detecting multiple targets. In particular, we allocate antennas of the MIMO radar to probe different targets simultaneously in a flexible manner based on the performance metric of relative entropy. Two antenna allocation schemes are proposed. In the first scheme, each antenna is allocated to illuminate a proper target over the entire illumination time, so that the detection performance of each target is guaranteed. The problem is formulated as a minimum makespan scheduling problem in the combinatorial optimization framework. Antenna allocation is implemented through a branch-and-bound algorithm and an enhanced factor 2 algorithm. In the second scheme, called antenna-time allocation, each antenna is allocated to illuminate different targets with different illumination time. Both antenna allocation and time allocation are optimized based on illumination probabilities. Over a large range of transmitted power, target fluctuations and target numbers, both of the proposed antenna allocation schemes outperform the scheme without antenna allocation. Moreover, the antenna-time allocation scheme achieves a more robust detection performance than branch-and-bound algorithm and the enhanced factor 2 algorithm when the target number changes. PMID:25350505

  4. Optimal coordination method of opportunistic array radars for multi-target-tracking-based radio frequency stealth in clutter

    NASA Astrophysics Data System (ADS)

    Zhang, Zhenkai; Salous, Sana; Li, Hailin; Tian, Yubo

    2015-11-01

    Opportunistic array radar is a new radar system that can improve the modern radar performance effectively. In order to improve its radio frequency stealth ability, a novel coordination method of opportunistic array radars in the network for target tracking in clutter is presented. First, the database of radar cross section for targets is built, then the signal-to-noise ratio for netted radars is computed according to the radar cross section and range of target. Then the joint probabilistic data association algorithm of tracking is improved with consideration of emitted power of the opportunistic array radar, which has a main impact on detection probability for tracking in clutter. Finally, with the help of grey relational grade and covariance control, the opportunistic array radar with the minimum radiated power will be selected for better radio frequency stealth performance. Simulation results show that the proposed algorithm not only has excellent tracking accuracy in clutter but also saves much more radiated power comparing with other methods.

  5. Textural feature based target detection in through-the-wall radar imagery

    NASA Astrophysics Data System (ADS)

    Sengur, A.; Amin, M.; Ahmad, F.; Sévigny, P.; DiFilippo, D.

    2013-05-01

    Stationary target detection in through-the-wall radar imaging (TWRI) using image segmentation techniques has recently been considered in the literature. Specifically, histogram thresholding methods have been used to aid in removing the clutter, resulting in `clean' radar images with target regions only. In this paper, we show that histogram thresholding schemes are effective only against clutter regions, which are distinct from target regions. Target detection using these methods becomes challenging, if not impossible, in the presence of multipath ghosts and clutter that closely mimics the target in size and intensity. Because of the small variations between the target regions and such clutter and multipath ghosts, we propose a textural feature based classifier for through-the-wall target detection. The feature based scheme is applied as a follow-on step after application of histogram thresholding techniques. The training set consists of feature vectors based on gray level co-occurrence matrices corresponding to the target and ghost/clutter image regions. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Performance of the proposed scheme is evaluated using real-data collected with Defence Research and Development Canada's vehicle-borne TWRI system. The results show that the proposed textural feature based method yields much improved results compared to histogram thresholding based segmentation methods for the considered cases.

  6. Cognitive processing for nonlinear radar

    NASA Astrophysics Data System (ADS)

    Martone, Anthony; Ranney, Kenneth; Hedden, Abigail; Mazzaro, Gregory; McNamara, David

    2013-05-01

    An increasingly cluttered electromagnetic environment (EME) is a growing problem for radar systems. This problem is becoming critical as the available frequency spectrum shrinks due to growing wireless communication device usage and changing regulations. A possible solution to these problems is cognitive radar, where the cognitive radar learns from the environment and intelligently modifies the transmit waveform. In this paper, a cognitive nonlinear radar processing framework is introduced where the main components of this framework consist of spectrum sensing processing, target detection and classification, and decision making. The emphasis of this paper is to introduce a spectrum sensing processing technique that identifies a transmit-receive frequency pair for nonlinear radar. It will be shown that the proposed technique successfully identifies a transmit-receive frequency pair for nonlinear radar from data collected from the EME.

  7. Target detection using radar images of an airport surface

    NASA Astrophysics Data System (ADS)

    Schultz, Hayden B.; Wyschogrod, Daniel; Harman, William H.; Sasiela, Richard J.; Bush, Richard W.

    1994-07-01

    Automation aids which increase the efficiency of the controller and enhance safety are being sought by the Federal Aviation Administration (FAA). This paper describes the target detection algorithms developed by the MIT Lincoln Laboratory as part of the airport surface traffic automation (ASTA) and runway surface safety light system (RSLS) programs sponsored by the FAA that were demonstrated at Logan International Airport in Boston, Mass. from September 1992 through December 1993. A companion paper to this conference describes the ASTA and RSLS system demonstration. Another companion paper describes the tracking algorithms. Real-time, parallel processing implementations of these surveillance algorithms are written in C++ on a Silicon Graphics Inc. Unix multiprocessor. The heavy reliance on commercial hardware, standard operating systems, object oriented design, and high-level computer languages allows a rapid transition from a research environment to a production environment.

  8. Detection of a target in a rock formation using the radar fracture mapping tool

    SciTech Connect

    Duda, L.E.; Uhl, J.E.; Gabaldon, J.; Chang, Hsi-Tien

    1988-01-01

    A method to locate fractures adjacent to, but not intersecting, an uncased wellbore would be a great aid to the geothermal industry. A prototype downhole radar probe was recently completed with the aim of locating fractures near a single wellbore. This probe, operating in the pulse mode with a bandwidth of 30 to 300 MHz, contains two identical directional antennas. As with any prototype instrumentation, extensive field work is required to completely understand the characteristics of the system. A first step in that understanding is to operate the instrument under known or controlled conditions. In this paper, some tests of the radar probe in a travertine quarry using a known target are reported. In the tests, the target is clearly detected from a borehole located 14 ft away. 12 refs., 5 figs., 1 tab.

  9. Alternative procedure for detecting stationary targets with ultrawideband foliage-penetration radar

    NASA Astrophysics Data System (ADS)

    Ranney, Kenneth I.; Khatri, Hiralal; Nguyen, Lam H.

    2001-08-01

    The Army Research Laboratory has investigated various phenomenology-based approaches for improving the detection of targets in wide-angle, ultra-wideband foliage penetration synthetic aperture radar (SAR) data. The approach presented here exploits the aspect-dependent reflectivity of vehicles, by filtering the SAR image data to obtain sub-aperture images from the original full-aperture radar image. These images represent the images of the target as seen by the sub-aperture SAR from two different locations (squint angles). We present a straightforward approach to extending an existing collection of features for a quadratic polynomial discriminator with features calculated from these two, lower-resolution sub-aperture SAR data images. We describe a method for generating the modified features and assess their potential contribution to improved probability of detection.

  10. Radar Constant-Modulus Waveform Design with Prior Information of the Extended Target and Clutter.

    PubMed

    Yue, Wenzhen; Zhang, Yan; Liu, Yimin; Xie, Jingwen

    2016-01-01

    Radar waveform design is of great importance for radar system performances and has drawn considerable attention recently. Constant modulus is an important waveform design consideration, both from the point of view of hardware realization and to allow for full utilization of the transmitter's power. In this paper, we consider the problem of constant-modulus waveform design for extended target detection with prior information about the extended target and clutter. At first, we propose an arbitrary-phase unimodular waveform design method via joint transmitter-receiver optimization. We exploit a semi-definite relaxation technique to transform an intractable non-convex problem into a convex problem, which can then be efficiently solved. Furthermore, quadrature phase shift keying waveform is designed, which is easier to implement than arbitrary-phase waveforms. Numerical results demonstrate the effectiveness of the proposed methods. PMID:27322275

  11. Radar Constant-Modulus Waveform Design with Prior Information of the Extended Target and Clutter

    PubMed Central

    Yue, Wenzhen; Zhang, Yan; Liu, Yimin; Xie, Jingwen

    2016-01-01

    Radar waveform design is of great importance for radar system performances and has drawn considerable attention recently. Constant modulus is an important waveform design consideration, both from the point of view of hardware realization and to allow for full utilization of the transmitter’s power. In this paper, we consider the problem of constant-modulus waveform design for extended target detection with prior information about the extended target and clutter. At first, we propose an arbitrary-phase unimodular waveform design method via joint transmitter-receiver optimization. We exploit a semi-definite relaxation technique to transform an intractable non-convex problem into a convex problem, which can then be efficiently solved. Furthermore, quadrature phase shift keying waveform is designed, which is easier to implement than arbitrary-phase waveforms. Numerical results demonstrate the effectiveness of the proposed methods. PMID:27322275

  12. Through-the-Wall Localization of a Moving Target by Two Independent Ultra Wideband (UWB) Radar Systems

    PubMed Central

    Kocur, Dušan; Švecová, Mária; Rovňáková, Jana

    2013-01-01

    In the case of through-the-wall localization of moving targets by ultra wideband (UWB) radars, there are applications in which handheld sensors equipped only with one transmitting and two receiving antennas are applied. Sometimes, the radar using such a small antenna array is not able to localize the target with the required accuracy. With a view to improve through-the-wall target localization, cooperative positioning based on a fusion of data retrieved from two independent radar systems can be used. In this paper, the novel method of the cooperative localization referred to as joining intersections of the ellipses is introduced. This method is based on a geometrical interpretation of target localization where the target position is estimated using a properly created cluster of the ellipse intersections representing potential positions of the target. The performance of the proposed method is compared with the direct calculation method and two alternative methods of cooperative localization using data obtained by measurements with the M-sequence UWB radars. The direct calculation method is applied for the target localization by particular radar systems. As alternative methods of cooperative localization, the arithmetic average of the target coordinates estimated by two single independent UWB radars and the Taylor series method is considered. PMID:24021968

  13. Location detection and tracking of moving targets by a 2D IR-UWB radar system.

    PubMed

    Nguyen, Van-Han; Pyun, Jae-Young

    2015-01-01

    In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking. PMID:25808773

  14. Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System

    PubMed Central

    Nguyen, Van-Han; Pyun, Jae-Young

    2015-01-01

    In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking. PMID:25808773

  15. A new look at spotlight mode synthetic aperture radar as tomography: imaging 3-D targets.

    PubMed

    Jakowatz, C V; Thompson, P A

    1995-01-01

    A new 3D tomographic formulation of spotlight mode synthetic aperture radar (SAR) is developed. This extends the pioneering work of Munson et al. (1983), who first formally described SAR in terms of tomography but who made the simplifying assumption that the target scene was 2D. The present authors treat the more general and practical case in which the radar target reflectivities comprise a 3D function. The main goal is to demonstrate that the demodulated radar return data from a spotlight mode collection represent a certain set of samples of the 3D Fourier transform of the target reflectivity function and to do so using a tomographic paradigm instead of traditional range-Doppler analysis. They also show that the tomographic approach is useful in interpreting the reconstructed 2D SAR image corresponding to a 3D scene. Specifically, the well-known SAR phenomenon of layover is easily explained in terms of tomographic projections and is shown to be analogous to the projection effect in conventional optical imaging. PMID:18290021

  16. Low-frequency ultrawideband synthetic aperture radar: frequency subbanding for targets obscured by the ground

    NASA Astrophysics Data System (ADS)

    Happ, Lynn; Le, Francis; Ressler, Marc A.; Kappra, Karl A.

    1996-06-01

    The Army Research Laboratory (ARL) has been investigating the potential of ultra-wideband synthetic aperture radar (UWB SAR) technology to detect and classify targets concealed by subsurface targets and foliage. Our investigative approach is to collect high-quality precision data to support phenomenological investigations of electromagnetic wave propagation through dielectric media. These investigations, in turn, support the development of algorithms for automatic target recognition. In order to achieve these goals, ARL designed and built an impulse (very short pulse) radar to collect data at a variety of test sites to measure and analyze the responses from targets, clutter, and targets embedded in clutter. The UWB BoomSAR, mounted on a 150-foot-high mobile boom lift, collects the high-quality, precision data sets needed for understanding UWB SAR system requirements and foliage penetration and ground penetration phenomenology. The BoomSAR operates with over 1 gigahertz of bandwidth covering a spectrum from 40 MHz to 1 GHz and is fully polarimetric. This bandwidth contains low frequencies needed for ground penetration while also maintaining higher frequency coverage for high resolution imagery. This paper shows a GPEN target area from data collected at Yuma Proving Grounds, AZ in low- and high- frequency subbands.

  17. Automated target classification in high resolution dual frequency sonar imagery

    NASA Astrophysics Data System (ADS)

    Aridgides, Tom; Fernández, Manuel

    2007-04-01

    An improved computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The classified objects of 2 distinct strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution dual frequency sonar imagery. Three significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion algorithm was developed. Second, a Box-Cox nonlinear feature LLRT fusion algorithm was developed. The Box-Cox transformation consists of raising the features to a to-be-determined power. Third, a repeated application of a subset feature selection / feature orthogonalization / Volterra feature LLRT fusion block was utilized. It was shown that cascaded Volterra feature LLRT fusion of the CAD/CAC processing strings outperforms summing, baseline single-stage Volterra and Box-Cox feature LLRT algorithms, yielding significant improvements over the best single CAD/CAC processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate. Additionally, the robustness of cascaded Volterra feature fusion was demonstrated, by showing that the algorithm yields similar performance with the training and test sets.

  18. The effect of target and non-target similarity on neural classification performance: a boost from confidence

    PubMed Central

    Marathe, Amar R.; Ries, Anthony J.; Lawhern, Vernon J.; Lance, Brent J.; Touryan, Jonathan; McDowell, Kaleb; Cecotti, Hubert

    2015-01-01

    Brain computer interaction (BCI) technologies have proven effective in utilizing single-trial classification algorithms to detect target images in rapid serial visualization presentation tasks. While many factors contribute to the accuracy of these algorithms, a critical aspect that is often overlooked concerns the feature similarity between target and non-target images. In most real-world environments there are likely to be many shared features between targets and non-targets resulting in similar neural activity between the two classes. It is unknown how current neural-based target classification algorithms perform when qualitatively similar target and non-target images are presented. This study address this question by comparing behavioral and neural classification performance across two conditions: first, when targets were the only infrequent stimulus presented amongst frequent background distracters; and second when targets were presented together with infrequent non-targets containing similar visual features to the targets. The resulting findings show that behavior is slower and less accurate when targets are presented together with similar non-targets; moreover, single-trial classification yielded high levels of misclassification when infrequent non-targets are included. Furthermore, we present an approach to mitigate the image misclassification. We use confidence measures to assess the quality of single-trial classification, and demonstrate that a system in which low confidence trials are reclassified through a secondary process can result in improved performance. PMID:26347597

  19. Target discrimination method for passive bistatic radar using narrowband and low-frequency illuminator

    NASA Astrophysics Data System (ADS)

    Lin, Caiyong; Wang, Dinghe; Tian, Ruiqi; Bao, Qinglong; Chen, Zengping

    2015-01-01

    As most illuminators of opportunity are relatively narrowband and of low-frequency, passive bistatic radar (PBR) is so weak in target discrimination that it can hardly distinguish adjacent aircraft or ships. To solve this problem, we propose a matched filter-based method. This method uses the bistatic range of the target to construct the corresponding filter groups and then produces a two-dimensional image by correlating the echo signals. We finally convert the target discrimination problem to distinguish the peaks in the image. The proposed method overcomes the target discrimination problem for PBR using the narrowband and low-frequency illuminator. Simulation results indicate the effectiveness and validity of the proposed method in distinguishing adjacent targets.

  20. Fixed lag smoothing target tracking in clutter for a high pulse repetition frequency radar

    NASA Astrophysics Data System (ADS)

    Khan, Uzair; Shi, Yi Fang; Song, Taek Lyul

    2015-12-01

    A new method to smooth the target hybrid state with Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) in the presence of clutter for a high pulse repetition frequency (HPRF) radar is proposed. This method smooths the target state at fixed lag N and considers all feasible multi-scan target existence sequences in the temporal window of scans in order to smooth the target hybrid state. The smoothing window can be of any length N. The proposed method to smooth the target hybrid state at fixed lag is also applied to the enhanced multiple model (EMM) tracking algorithm. Simulation results indicate that the performance of fixed lag smoothing GMM-ITS significantly improves false track discrimination and root mean square errors (RMSEs).

  1. Clutter and target discrimination in forward-looking ground penetrating radar using sparse structured basis pursuits

    NASA Astrophysics Data System (ADS)

    Camilo, Joseph A.; Malof, Jordan M.; Torrione, Peter A.; Collins, Leslie M.; Morton, Kenneth D.

    2015-05-01

    Forward-looking ground penetrating radar (FLGPR) is a remote sensing modality that has recently been investigated for buried threat detection. FLGPR offers greater standoff than other downward-looking modalities such as electromagnetic induction and downward-looking GPR, but it suffers from high false alarm rates due to surface and ground clutter. A stepped frequency FLGPR system consists of multiple radars with varying polarizations and bands, each of which interacts differently with subsurface materials and therefore might potentially be able to discriminate clutter from true buried targets. However, it is unclear which combinations of bands and polarizations would be most useful for discrimination or how to fuse them. This work applies sparse structured basis pursuit, a supervised statistical model which searches for sets of bands that are collectively effective for discriminating clutter from targets. The algorithm works by trying to minimize the number of selected items in a dictionary of signals; in this case the separate bands and polarizations make up the dictionary elements. A structured basis pursuit algorithm is employed to gather groups of modes together in collections to eliminate whole polarizations or sensors. The approach is applied to a large collection of FLGPR data for data around emplaced target and non-target clutter. The results show that a sparse structure basis pursuits outperforms a conventional CFAR anomaly detector while also pruning out unnecessary bands of the FLGPR sensor.

  2. Ice-type classifications from airborne pulse-limited radar altimeter return waveform characteristics

    NASA Technical Reports Server (NTRS)

    Fedor, L. S.; Hayne, G. S.; Walsh, E. J.

    1989-01-01

    During mid-March 1978, the NASA C-130 aircraft was deployed to Eielson Air Force Base in Fairbanks, Alaska, to make a series of flights over ice in the Beaufort Sea. The radar altimeter data analyzed were obtained northeast of Mackenzie Bay on March 14th in the vicinity of 69.9 deg N, 134.2 deg W. The data were obtained with a 13.9 GHz radar altimeter developed under the NASA Advanced Applications Flight Experiments (AAFE) Program. This airborne radar was built as a forerunner of the Seasat radar altimeter, and utilized the same pulse compression technique. Pulse-limited radar data taken with the altimeter from 1500-m altitude over sea ice are registered to high-quality photography. The backscattered power is statistically related the surface conductivity and to the number of facets whose surface normal is directed towards the radar. The variations of the radar return waveform shape and signal level are correlated with the variation of the ice type determined from photography. The AAFE altimeter has demonstrated that the return waveform shape and signal level of an airborne pulse-limited altimeter at 13.9 GHz respond to sea ice type. The signal level responded dramatically to even a very small fracture in the ice, as long as it occurred directly at the altimeter nadir point. Shear zones and regions of significant compression ridging consistently produced low signal levels. The return waveforms frequently evidenced the characteristics of both specular and diffuse scattering, and there was an indication that the power backscattered at 3 deg off-nadir in a shear zone was actually somewhat higher than that from nadir.

  3. Measurement of projectile trajectory in dielectric target with micropower-impluse radar

    SciTech Connect

    Baum, D.W.; Kuklo, R.M.; Rosenbury, E.T.; Simonson, S.C.

    1997-11-20

    The micropower-impulse radar has been adapted for non-intrusive tracking of projectiles in dielectric targets. The main application of this technique is intended to be the validation of continuum mechanics simulation codes and material models used in the study of the interaction between high-velocity penetrators and concrete targets. Two experiments have been conducted in which a gun-launched 90-mm-diameter projectile was fired at velocities of 160 and 230 m/s into a cubical box filled with dry sand and tracked with the micropower-impulse radar. The system was adjusted so that a 2-m range in sand was divided into 511 timing intervals, which were swept every 0.1 ms. As the projectile took approximately 40 ms to come to rest this meant that there were 400measurements of its position. The CALE continuum mechanics simulation was used to model the projectile motion in the target, and close agreement was found with the measured trajectory.

  4. Multi-temporal airborne synthetic aperture radar data for crop classification

    NASA Technical Reports Server (NTRS)

    Foody, G. M.; Curran, P. J.; Groom, G. B.; Munro, D. C.

    1989-01-01

    This paper presents an approach to the classification of crop type using multitemporal airborne SAR data. Following radiometric correction of the data, the accuracy of a per-field crop classification reached 90 percent for three classes using data acquired on four dates. A comparable accuracy of 88 percent could be obtained for a classification of the same classes using data acquired on only two dates. Increasing the number of classes from three to seven reduced the classification accuracies to 55 percent and 69 percent when using data from two and four dates respectively.

  5. Precision targeting in guided munition using IR sensor and MmW radar

    NASA Astrophysics Data System (ADS)

    Sreeja, S.; Hablani, H. B.; Arya, H.

    2015-10-01

    Conventional munitions are not guided with sensors and therefore miss the target, particularly if the target is mobile. The miss distance of these munitions can be decreased by incorporating sensors to detect the target and guide the munition during flight. This paper is concerned with a Precision Guided Munition(PGM) equipped with an infrared sensor and a millimeter wave radar [IR and MmW, for short]. Three-dimensional flight of the munition and its pitch and yaw motion models are developed and simulated. The forward and lateral motion of a target tank on the ground is modeled as two independent second-order Gauss-Markov process. To estimate the target location on the ground and the line-of-sight rate to intercept it an Extended Kalman Filter is composed whose state vector consists of cascaded state vectors of missile dynamics and target dynamics. The line-of-sight angle measurement from the infrared seeker is by centroiding the target image in 40 Hz. The centroid estimation of the images in the focal plane is at a frequency of 10 Hz. Every 10 Hz, centroids of four consecutive images are averaged, yielding a time-averaged centroid, implying some measurement delay. The miss distance achieved by including by image processing delays is 1:45m.

  6. Study of the Effects of Target Geometry on Synthetic Aperture Radar Images using Simulation Studies

    NASA Astrophysics Data System (ADS)

    Tummala, K.; Jha, A. K.; Kumar, S.

    2014-11-01

    Synthetic aperture radar technology has revolutionized earth observation with very high resolutions of below 5m, making it possible to distinguish individual urban features like buildings and even cars on the surface of the earth. But, the difficulty in interpretation of these images has hindered their use. The geometry of target objects and their orientation with respect to the SAR sensor contribute enormously to unexpected signatures on SAR images. Geometry of objects can cause single, double or multiple reflections which, in turn, affect the brightness value on the SAR images. Occlusions, shadow and layover effects are present in the SAR images as a result of orientation of target objects with respect to the incident microwaves. Simulation of SAR images is the best and easiest way to study and understand the anomalies. This paper discusses synthetic aperture radar image simulation, with the study of effect of target geometry as the main aim. Simulation algorithm has been developed in the time domain to provide greater modularity and to increase the ease of implementation. This algorithm takes into account the sensor and target characteristics, their locations with respect to the earth, 3-dimensional model of the target, sensor velocity, and SAR parameters. two methods have been discussed to obtain position and velocity vectors of SAR sensor - the first, from the metadata of real SAR image used to verify the simulation algorithm, and the second, from satellite orbital parameters. Using these inputs, the SAR image coordinates and backscatter coefficients for each point on the target are calculated. The backscatter coefficients at target points are calculated based on the local incidence angles using Muhleman's backscatter model. The present algorithm has been successfully implemented on radarsat-2 image of San Francisco bay area. Digital elevation models (DEMs) of the area under consideration are used as the 3d models of the target area. DEMs of different

  7. Ranging and target detection performance through lossy media using an ultrawideband S-band through-wall sensing noise radar

    NASA Astrophysics Data System (ADS)

    Smith, Sonny; Narayanan, Ram M.

    2013-05-01

    An S-band noise radar has been developed for through-wall ranging and tracking of targets. Ranging to target is achieved by the cross-correlation between the time-delayed reflected return signal and the replica of the transmit signal; both are bandlimited ultrawideband (UWB) noise signals. Furthermore, successive scene subtraction allows for target tracking using the range profiles created by the cross-correlation technique. In this paper, we explore the performance of the radar system for target detection through varied, lossy media (e.g. a 4-inch thick brick wall and an 8-inch thick cinder-block wall) via correlation measurements using the S-band radar system. Moreover, we present a qualitative analysis of the S-band noise radar as operated under disparate testing configurations (i.e. different walls, targets, and distances.) with different antennas (e.g. dual polarized horns, helical antennas with different ground planes, etc.). In addition, we discuss key concepts of the noise radar design, considerations for an antenna choice, as well as experimental results for a few scenarios.

  8. An Improved Polarimetric Radar Rainfall Algorithm With Hydrometeor Classification Optimized For Rainfall Estimation

    NASA Astrophysics Data System (ADS)

    Cifelli, R.; Wang, Y.; Lim, S.; Kennedy, P.; Chandrasekar, V.; Rutledge, S. A.

    2009-05-01

    The efficacy of dual polarimetric radar for quantitative precipitation estimation (QPE) is firmly established. Specifically, rainfall retrievals using combinations of reflectivity (ZH), differential reflectivity (ZDR), and specific differential phase (KDP) have advantages over traditional Z-R methods because more information about the drop size distribution and hydrometeor type are available. In addition, dual-polarization radar measurements are generally less susceptible to error and biases due to the presence of ice in the sampling volume. A number of methods have been developed to estimate rainfall from dual-polarization radar measurements. However, the robustness of these techniques in different precipitation regimes is unknown. Because the National Weather Service (NWS) will soon upgrade the WSR 88-D radar network to dual-polarization capability, it is important to test retrieval algorithms in different meteorological environments in order to better understand the limitations of the different methodologies. An important issue in dual-polarimetric rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), a "blended" algorithm has been developed and used for a number of years to estimate rainfall based on ZH, ZDR, and KDP (Cifelli et al. 2002). The rainfall estimators for each sampling volume are chosen on the basis of fixed thresholds, which maximize the measurement capability of each polarimetric variable and combinations of variables. Tests have shown, however, that the retrieval is sensitive to the calculation of ice fraction in the radar volume via the difference reflectivity (ZDP - Golestani et al. 1989) methodology such that an inappropriate estimator can be selected in situations where radar echo is

  9. A New Methodology for 3D Target Detection in Automotive Radar Applications.

    PubMed

    Baselice, Fabio; Ferraioli, Giampaolo; Lukin, Sergyi; Matuozzo, Gianfranco; Pascazio, Vito; Schirinzi, Gilda

    2016-01-01

    Today there is a growing interest in automotive sensor monitoring systems. One of the main challenges is to make them an effective and valuable aid in dangerous situations, improving transportation safety. The main limitation of visual aid systems is that they do not produce accurate results in critical visibility conditions, such as in presence of rain, fog or smoke. Radar systems can greatly help in overcoming such limitations. In particular, imaging radar is gaining interest in the framework of Driver Assistance Systems (DAS). In this manuscript, a new methodology able to reconstruct the 3D imaged scene and to detect the presence of multiple targets within each line of sight is proposed. The technique is based on the use of Compressive Sensing (CS) theory and produces the estimation of multiple targets for each line of sight, their range distance and their reflectivities. Moreover, a fast approach for 2D focus based on the FFT algorithm is proposed. After the description of the proposed methodology, different simulated case studies are reported in order to evaluate the performances of the proposed approach. PMID:27136558

  10. A New Methodology for 3D Target Detection in Automotive Radar Applications

    PubMed Central

    Baselice, Fabio; Ferraioli, Giampaolo; Lukin, Sergyi; Matuozzo, Gianfranco; Pascazio, Vito; Schirinzi, Gilda

    2016-01-01

    Today there is a growing interest in automotive sensor monitoring systems. One of the main challenges is to make them an effective and valuable aid in dangerous situations, improving transportation safety. The main limitation of visual aid systems is that they do not produce accurate results in critical visibility conditions, such as in presence of rain, fog or smoke. Radar systems can greatly help in overcoming such limitations. In particular, imaging radar is gaining interest in the framework of Driver Assistance Systems (DAS). In this manuscript, a new methodology able to reconstruct the 3D imaged scene and to detect the presence of multiple targets within each line of sight is proposed. The technique is based on the use of Compressive Sensing (CS) theory and produces the estimation of multiple targets for each line of sight, their range distance and their reflectivities. Moreover, a fast approach for 2D focus based on the FFT algorithm is proposed. After the description of the proposed methodology, different simulated case studies are reported in order to evaluate the performances of the proposed approach. PMID:27136558

  11. Characterizing geolocation ambiguity responses in synthetic aperture radar: ground moving target indication

    NASA Astrophysics Data System (ADS)

    Holston, Matthew E.; Minardi, Michael J.; Temple, Michael A.; Saville, Michael A.

    2007-04-01

    Single-channel synthetic aperture radar (SAR) can provide high quality, focused images of moving targets by utilizing advanced SAR-GMTI techniques that focus all constant velocity targets into a three-dimensional space indexed by range, cross-range and cross-range velocity. However, an inherent geolocation ambiguity exists in that multiple, distinct moving targets may posses identical range versus time responses relative to a constant velocity collection platform. Although these targets are uniquely located within a four-dimensional space (x-position, y-position, x-velocity, and y-velocity), their responses are focused and mapped to the same three-dimensional position in the SAR-GMTI image cube. Previous research has shown that circular SAR (CSAR) collection geometry is one way to break this ambiguity and creates a four-dimensional detection space. This research determines the target resolution available in the detection space as a function of different collection parameters. A metric is introduced to relate the resolvability of multiple target responses for various parametric combinations, i.e., changes in key collection parameters such as integration time, slant range, look angle, and carrier frequency.

  12. Heterodyne efficiency for a coherent laser radar with diffuse or aerosol targets

    NASA Technical Reports Server (NTRS)

    Frehlich, R. G.

    1993-01-01

    The performance of a Coherent Laser Radar is determined by the statistics of the coherent Doppler signal. The heterodyne efficiency is an excellent indication of performance because it is an absolute measure of beam alignment and is independent of the transmitter power, the target backscatter coefficient, the atmospheric attenuation, and the detector quantum efficiency and gain. The theoretical calculation of heterodyne efficiency for an optimal monostatic lidar with a circular aperture and Gaussian transmit laser is presented including beam misalignment in the far-field and near-field regimes. The statistical behavior of estimates of the heterodyne efficiency using a calibration hard target are considered. For space based applications, a biased estimate of heterodyne efficiency is proposed that removes the variability due to the random surface return but retains the sensitivity to misalignment. Physical insight is provided by simulation of the fields on the detector surface. The required detector calibration is also discussed.

  13. Experimental demonstration of a multi-target detection technique using an X-band optically steered phased array radar.

    PubMed

    Shi, Nuannuan; Li, Ming; Deng, Ye; Zhang, Lihong; Sun, Shuqian; Tang, Jian; Li, Wei; Zhu, Ninghua

    2016-06-27

    An X-band optically-steered phased array radar is developed to demonstrate high resolution multi-target detection. The beam forming is implemented based on wavelength-swept true time delay (TTD) technique. The beam forming system has a wide direction tuning range of ± 54 degree, low magnitude ripple of ± 0.5 dB and small delay error of 0.13 ps/nm. To further verify performance of the proposed optically-steered phased array radar, three experiments are then carried out to implement the single and multiple target detection. A linearly chirped X-band microwave signal is used as radar signal which is finally compressed at the receiver to improve the detection accuracy. The ranging resolution for multi-target detection is up to 2 cm within the measuring distance over 4 m and the azimuth angle error is less than 4 degree. PMID:27410597

  14. radR: an open-source platform for acquiring and analysing data on biological targets observed by surveillance radar

    PubMed Central

    2010-01-01

    Background Radar has been used for decades to study movement of insects, birds and bats. In spite of this, there are few readily available software tools for the acquisition, storage and processing of such data. Program radR was developed to solve this problem. Results Program radR is an open source software tool for the acquisition, storage and analysis of data from marine radars operating in surveillance mode. radR takes time series data with a two-dimensional spatial component as input from some source (typically a radar digitizing card) and extracts and retains information of biological relevance (i.e. moving targets). Low-level data processing is implemented in "C" code, but user-defined functions written in the "R" statistical programming language can be called at pre-defined steps in the calculations. Output data formats are designed to allow for future inclusion of additional data items without requiring change to C code. Two brands of radar digitizing card are currently supported as data sources. We also provide an overview of the basic considerations of setting up and running a biological radar study. Conclusions Program radR provides a convenient, open source platform for the acquisition and analysis of radar data of biological targets. PMID:20977735

  15. The Effect of Synthetic Aperture Radar Image Resolution on Target Discrimination

    NASA Astrophysics Data System (ADS)

    Terzuoli, Andrew; McGowan, John; Gustafson, Steven; Jackson, Julie; Martin, Richard

    This research details the effect of spatial resolution on target discrimination in Synthetic Aper-ture Radar (SAR) images. Multiple SAR image chips containing targets and non-targets are used to test a baseline Automatic Target Recognition (ATR) system with reduced spatial reso-lution obtained by lowering the pixel count or synthesizing a degraded image. The pixel count is lowered by averaging groups of adjoining pixels to form a new single value. The degraded image is synthesized by low-pass filtering the image frequency space and then lowering the pixel count. A two parameter Constant False Alarm Rate (CFAR) detector is tested, and three different types of feature spaces; size, contrast, and texture; are used to train a linear classifier. The results are scored using the Area Under the Receiver Operator Characteristic (AUROC) curve. The CFAR detector is shown to perform better at lower resolution. All three feature sets together performed well with the degradation of resolution; separately the sets had different performances. The texture features performed best because they do not rely on the number of pixels on the target, while the size features performed worst for the same reason. The contrast features yielded improved performance when the resolution was slightly reduced. The views expressed in this article are those of the authors and do not reflect the official policy of the U.S. Air Force, U.S. Department of Defense, or the U.S. Government.

  16. The effect of synthetic aperture radar image resolution on target discrimination

    NASA Astrophysics Data System (ADS)

    McGowan, John E.; Gustafson, Steven C.; Jackson, Julie A.; Terzuoli, Andrew J., Jr.

    2010-04-01

    This paper details the effect of spatial resolution on target discrimination in Synthetic Aperture Radar (SAR) images. Multiple SAR image chips, containing targets and non-targets, are used to test a baseline Automatic Target Recognition (ATR) system with reduced spatial resolution obtained by lowering the pixel count or synthesizing a degraded image. The pixel count is lowered by averaging groups of adjoining pixels to form a new single value. The degraded image is synthesized by low-pass-filtering the image frequency space and then lowering the pixel count. To train a linear classifier, a two-parameter Constant False Alarm Rate (CFAR) detector is tested, and three different types of feature spaces, are used: size, contrast, and texture. The results are scored using the Area Under the Receiver Operator Characteristic (AUROC) curve. The CFAR detector is shown to perform better at lower resolution. All three feature sets together performed well with the degradation of resolution; separately the sets had different performances. The texture features performed best because they do not rely on the number of pixels on the target, while the size features performed the worst for the same reason. The contrast features yielded improved performance when the resolution was slightly reduced.

  17. Fuzzy detection and classification of dangerous weather phenomena using dual-polarimetric radar measurements

    NASA Astrophysics Data System (ADS)

    Tho Dang, Van; Yanovsky, F. J.

    2009-06-01

    A fuzzy detector and classifier of dangerous weather phenomena based on polarimetric radar measurements are described in this paper. Five polarimetric radar measurands, namely, horizontal reflectivity factor, differential reflectivity factor, linear depolarization ratio, specific differential phase, cross-correlation coefficient and altitude of resolution volume serve as inputs of the fuzzy detector and classifier. The output of the fuzzy detector and classifier is one of 8 possible classes: 0) No dangerous weather phenomenon is detected; 1) Lightning; 2) Aircraft icing; 3) Hail; 4) Hail+rain; 5) Heavy rain; 6) Wet snow; 7) Dense snow. A neural network backpropagation algorithm is also considered for training the fuzzy detector and classifier in case of having verified data.

  18. Situational awareness sensor management of space-based EO/IR and airborne GMTI radar for road targets tracking

    NASA Astrophysics Data System (ADS)

    El-Fallah, A.; Zatezalo, A.; Mahler, R.; Mehra, R. K.; Pham, K.

    2010-04-01

    Dynamic sensor management of heterogeneous and distributed sensors presents a daunting theoretical and practical challenge. We present a Situational Awareness Sensor Management (SA-SM) algorithm for the tracking of ground targets moving on a road map. It is based on the previously developed information-theoretic Posterior Expected Number of Targets of Interest (PENTI) objective function, and utilizes combined measurements form an airborne GMTI radar, and a space-based EO/IR sensor. The resulting filtering methods and techniques are tested and evaluated. Different scan rates for the GMTI radar and the EO/IR sensor are evaluated and compared.

  19. Small battery operated unattended radar sensor for security systems

    NASA Astrophysics Data System (ADS)

    Plummer, Thomas J.; Brady, Stephen; Raines, Robert

    2013-06-01

    McQ has developed, tested, and is supplying to Unattended Ground Sensor (UGS) customers a new radar sensor. This radar sensor is designed for short range target detection and classification. The design emphasis was to have low power consumption, totally automated operation, a very high probability of detection coupled with a very low false alarm rate, be able to locate and track targets, and have a price compatible with the UGS market. The radar sensor complements traditional UGS sensors by providing solutions for scenarios that are difficult for UGS. The design of this radar sensor and the testing are presented in this paper.

  20. Fly eye radar or micro-radar sensor technology

    NASA Astrophysics Data System (ADS)

    Molchanov, Pavlo; Asmolova, Olga

    2014-05-01

    To compensate for its eye's inability to point its eye at a target, the fly's eye consists of multiple angularly spaced sensors giving the fly the wide-area visual coverage it needs to detect and avoid the threats around him. Based on a similar concept a revolutionary new micro-radar sensor technology is proposed for detecting and tracking ground and/or airborne low profile low altitude targets in harsh urban environments. Distributed along a border or around a protected object (military facility and buildings, camp, stadium) small size, low power unattended radar sensors can be used for target detection and tracking, threat warning, pre-shot sniper protection and provides effective support for homeland security. In addition it can provide 3D recognition and targets classification due to its use of five orders more pulses than any scanning radar to each space point, by using few points of view, diversity signals and intelligent processing. The application of an array of directional antennas eliminates the need for a mechanical scanning antenna or phase processor. It radically decreases radar size and increases bearing accuracy several folds. The proposed micro-radar sensors can be easy connected to one or several operators by point-to-point invisible protected communication. The directional antennas have higher gain, can be multi-frequency and connected to a multi-functional network. Fly eye micro-radars are inexpensive, can be expendable and will reduce cost of defense.

  1. Moving target imaging by both Ka-band and Ku-band high-resolution radars

    NASA Astrophysics Data System (ADS)

    Zhang, Yunhua; Zhai, Wenshuai; Zhang, Xiangkun; Shi, Xiaojin; Gu, Xiang; Jiang, Jingshan

    2011-11-01

    The experimental work on testing the wide-band transmitters and receivers developed for Ka-band and Ku-band radar systems, as well as the signal processing algorithms were introduced. A city light-railway train was selected as the imaged target. The wide-band transmitters and receivers were designed based on the stepped-frequency chirp signal (SFCS) with 2GHz bandwidth synthesized. The Super-SVA technique was used to deal with the case of transmitting SFCS with band gaps between subchirps for purpose of achieving the same bandwidth using as less as possible subpulses. Both Ka-band and Ku-band high-resolution radar images were obtained, which show that Ka-band images are much clear than that of Ku-band as we expect. There are two reasons to explaining this, one reason is due to the electromagnetic scattering of train itself are different for Ka-band and Ku-band frequencies, and the other reason is due to the interactions, i.e. multi-reflection or multi-scattering between the train and the side metal fences or the lamp post are different.

  2. Moving Target Indication via RADARSAT-2 Multichannel Synthetic Aperture Radar Processing

    NASA Astrophysics Data System (ADS)

    Chiu, S.; Dragošević, M. V.

    2009-12-01

    With the recent launches of the German TerraSAR-X and the Canadian RADARSAT-2, both equipped with phased array antennas and multiple receiver channels, synthetic aperture radar, ground moving target indication (SAR-GMTI) data are now routinely being acquired from space. Defence R&D Canada has been conducting SAR-GMTI trials to assess the performance and limitations of the RADARSAT-2 GMTI system. Several SAR-GMTI modes developed for RADARSAT-2 are described and preliminary test results of these modes are presented. Detailed equations of motion of a moving target for multiaperture spaceborne SAR geometry are derived and a moving target parameter estimation algorithm developed for RADARSAT-2 (called the Fractrum Estimator) is presented. Limitations of the simple dual-aperture SAR-GMTI mode are analysed as a function of the signal-to-noise ratio and target speed. Recently acquired RADARSAT-2 GMTI data are used to demonstrate the capability of different system modes and to validate the signal model and the algorithm.

  3. Motion parameter estimation of multiple ground moving targets in multi-static passive radar systems

    NASA Astrophysics Data System (ADS)

    Subedi, Saurav; Zhang, Yimin D.; Amin, Moeness G.; Himed, Braham

    2014-12-01

    Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions, making them difficult to reliably estimate motion parameters of ground moving targets. On the other hand, the availability of multiple spatially separated illuminators of opportunity provides a means to achieve multi-static diversity and overall signal enhancement. In this paper, we consider the problem of estimating motion parameters, including velocity and acceleration, of multiple closely located ground moving targets in a typical MPR platform with focus on weak signal conditions, where traditional time-frequency analysis-based methods become unreliable or infeasible. The underlying problem is reformulated as a sparse signal reconstruction problem in a discretized parameter search space. While the different bistatic links have distinct Doppler signatures, they share the same set of motion parameters of the ground moving targets. Therefore, such motion parameters act as a common sparse support to enable the exploitation of group sparsity-based methods for robust motion parameter estimation. This provides a means of combining signal energy from all available illuminators of opportunity and, thereby, obtaining a reliable estimation even when each individual signal is weak. Because the maximum likelihood (ML) estimation of motion parameters involves a multi-dimensional search and its performance is sensitive to target position errors, we also propose a technique that decouples the target motion parameters, yielding a two-step process that sequentially estimates the acceleration and velocity vectors with a reduced dimensionality of the parameter search space. We compare the performance of the sequential method against the ML estimation with the consideration of imperfect knowledge of the initial target positions. The Cramér-Rao bound (CRB) of the underlying parameter estimation problem is derived for a general multiple-target scenario in an MPR system

  4. High Bandwidth, Multi-Purpose Passive Radar Receiver Design For Aerospace and Geoscience Targets

    NASA Astrophysics Data System (ADS)

    Vertatschitsch, Laura

    Passive radar permits inexpensive and stealthy detection and tracking of aerospace and geoscience targets. Transmitters of opportunity such as commercial FM broadcast, DTV broadcast, and cell phone towers are already illuminating many populated areas with continuous power. Passive radar receivers can be located at a distance from the transmitter, and can sense this direct transmission as well as any reflections from ground clutter, aircraft, ionospheric turbulence and meteor trails. The 100% duty cycle allows for long coherent integration, increasing the sensitivity of these instruments greatly. Traditional radar receivers employ analog front end downconverters to translate the radio frequency spectrum to an intermediate frequency (IF) for sampling and signal processing. Such downconverters limit the spectrum available for study, and can introduce nonlinearities which limit the detectability of weak signals in the presence of strong signals. With suitably fast digitizers one can bypass the downconversion stage completely. Very fast digitizers may have relatively few bits, but precision is recovered in subsequent signal processing. We present a new passive radar receiver designed to utilize a broad spectrum of commercial transmitters without the use of a front end analog downconverter. The receiver centers around a Reconfigurable Open Architecture Computing Hardware (ROACH) board developed by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) group. Fast sampling rates (8-bit samples as fast as 3 GSps) combined with 640 multiply/addition operations on the Virtex-5 FPGA centered on the ROACH allows for coherent processing of broad spectrum and dynamic decision-making on one device all while sharing a single front end, putting this device on the cutting edge of wideband receiver technology. The radar is also designed to support mobile operation. It fits within a 19'' rack, it is equipped with solid state hard drives, and can run off an

  5. Joint angle and Doppler frequency estimation of coherent targets in monostatic MIMO radar

    NASA Astrophysics Data System (ADS)

    Cao, Renzheng; Zhang, Xiaofei

    2015-05-01

    This paper discusses the problem of joint direction of arrival (DOA) and Doppler frequency estimation of coherent targets in a monostatic multiple-input multiple-output radar. In the proposed algorithm, we perform a reduced dimension (RD) transformation on the received signal first and then use forward spatial smoothing (FSS) technique to decorrelate the coherence and obtain joint estimation of DOA and Doppler frequency by exploiting the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. The joint estimated parameters of the proposed RD-FSS-ESPRIT are automatically paired. Compared with the conventional FSS-ESPRIT algorithm, our RD-FSS-ESPRIT algorithm has much lower complexity and better estimation performance of both DOA and frequency. The variance of the estimation error and the Cramer-Rao Bound of the DOA and frequency estimation are derived. Simulation results show the effectiveness and improvement of our algorithm.

  6. Development of two-dimensional parametric radar signal modeling and estimation techniques with application to target identification

    NASA Astrophysics Data System (ADS)

    Sacchini, Joseph J.

    1992-09-01

    One and two dimensional signal processing models and algorithms which are utilized in the Radar Target Identification Problem are developed. A basic assumption of this work is that the high-frequency scattering from a radar target, such as an aircraft, land-based vehicle, or ship, is comprised of the sum of the scattering from a finite number of canonical scattering centers, each with a specific location and identity. By high-frequency it is meant that the overall size of the target is at least one wavelength. The scattering center assumption is more valid as the individual scattering centers become more electrically isolated. If two individual scattering centers are electrically close, then their combined response is, in general, not the sum of their individual responses. First, this dissertation investigates the electromagnetic scattering characteristics of canonical scattering centers. Canonical scattering centers are scattering centers on a target which account for the vast majority of the scattering from that target in the high-frequency case. Some of the targets of interest in this work are aircraft, tanks, trucks, automobiles, and ships. Predominant scattering centers on these targets include corners, edges, plates, dihedrals, trihedrals, and cylinders. The scattering centers are described by their scattering characteristics as functions of angle, frequency, and polarization. Second, this dissertation develops a two-dimensional (2-D) signal processing technique for locating and characterizing scattering centers from radar data. The radar gathers scattering data of a target at both multiple frequencies and multiple angles. This type of data is gathered (in raw form) by both Synthetic Aperture Radars and Inverse Synthetic Aperture Radars. The 2-D signal processing technique developed here is based on a 2-D extension of a total least squares (TLS) solution to a Prony Model and is called the 2-D TLS-Prony Technique. This technique can use single or multiple

  7. Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

    NASA Astrophysics Data System (ADS)

    Ceballos, G. A.; Hernández, L. F.

    2015-04-01

    Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

  8. Advanced EMI models for survey data processing: targets detection and classification

    NASA Astrophysics Data System (ADS)

    Shubitidze, F.; Barrowes, B. E.; Wang, Yinlin; Shamatava, Irma; Sigman, J. B.; O'Neill, K.

    2016-05-01

    This paper describes procedures and approaches our team took to demonstrate the capability of advanced electromagnetic induction (EMI) forward and inverse models to perform subsurface metallic objects picking and classification at live-UXO sites from dynamic data sets. Over the past seven years, blind classification tests at live-UXO sites have revealed two main challenges: 1) consistent selection of targets for cued interrogation, (e.g., for the recent SWPG2 study, two independent performers that processed the same MetalMapper dynamic data picked different targets for cued interrogation); and 2) positioning of the cued sensor close enough to the actual cued target to accurately perform classification (particularly when multiple targets or magnetic soils are present). To overcome these problems, in this paper we introduced an innovative and robust approach for subsurface metallic targets picking and classification from dynamic data sets. This approach first inverts for target locations and polarizabilities from each dynamic data point, and then clusters the inverted locations and defines each cluster as a target/source. Finally, the method uses the extracted polarizabilities for classifying UXO from non-UXO items. The studies are done for the 2x2 TEMTADS dynamic data set collected at Camp Hale, CO. The targets picking and classification results are illustrated and validated against ground truth.

  9. Recognizing subsurface target responses in ground penetrating radar data using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Rayn T.; Morton, Kenneth D.; Collins, Leslie M.; Torrione, Peter A.

    2015-05-01

    Improved performance in the discrimination of buried threats using Ground Penetrating Radar (GPR) data has recently been achieved using features developed for applications in computer vision. These features, designed to characterize local shape information in images, have been utilized to recognize patches that contain a target signature in two-dimensional slices of GPR data. While these adapted features perform very well in this GPR application, they were not designed to specifically differentiate between target responses and background GPR data. One option for developing a feature specifically designed for target differentiation is to manually design a feature extractor based on the physics of GPR image formation. However, as seen in the historical progression of computer vision features, this is not a trivial task. Instead, this research evaluates the use of convolutional neural networks (CNNs) applied to two-dimensional GPR data. The benefit of using a CNN is that features extracted from the data are a learned parameter of the system. This has allowed CNN implementations to achieve state of the art performance across a variety of data types, including visual images, without the need for expert designed features. However, the implementation of a CNN must be done carefully for each application as network parameters can cause performance to vary widely. This paper presents results from using CNNs for object detection in GPR data and discusses proper parameter settings and other considerations.

  10. Distributed micro-radar system for detection and tracking of low-profile, low-altitude targets

    NASA Astrophysics Data System (ADS)

    Gorwara, Ashok; Molchanov, Pavlo

    2016-05-01

    Proposed airborne surveillance radar system can detect, locate, track, and classify low-profile, low-altitude targets: from traditional fixed and rotary wing aircraft to non-traditional targets like unmanned aircraft systems (drones) and even small projectiles. Distributed micro-radar system is the next step in the development of passive monopulse direction finder proposed by Stephen E. Lipsky in the 80s. To extend high frequency limit and provide high sensitivity over the broadband of frequencies, multiple angularly spaced directional antennas are coupled with front end circuits and separately connected to a direction finder processor by a digital interface. Integration of antennas with front end circuits allows to exclude waveguide lines which limits system bandwidth and creates frequency dependent phase errors. Digitizing of received signals proximate to antennas allows loose distribution of antennas and dramatically decrease phase errors connected with waveguides. Accuracy of direction finding in proposed micro-radar in this case will be determined by time accuracy of digital processor and sampling frequency. Multi-band, multi-functional antennas can be distributed around the perimeter of a Unmanned Aircraft System (UAS) and connected to the processor by digital interface or can be distributed between swarm/formation of mini/micro UAS and connected wirelessly. Expendable micro-radars can be distributed by perimeter of defense object and create multi-static radar network. Low-profile, lowaltitude, high speed targets, like small projectiles, create a Doppler shift in a narrow frequency band. This signal can be effectively filtrated and detected with high probability. Proposed micro-radar can work in passive, monostatic or bistatic regime.

  11. Study on Landscape Freeze/Thaw Classification and its Spatial Scale Effects using Satellite L-band radar observations over Alaska

    NASA Astrophysics Data System (ADS)

    Du, J.; Kimball, J. S.; Azarderakhsh, M.; Dunbar, R.; Moghaddam, M.; McDonald, K. C.; Kim, Y.

    2013-12-01

    Spatial and temporal variability in landscape freeze-thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological and biogeochemical processes. With the development of new generation space-borne remote sensing instruments, future L-band missions including the NASA Soil Moisture Active and Passive (SMAP) mission will provide new operational retrievals of landscape FT state dynamics at relatively fine (3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with two-layer and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification over Alaska using finer scale (100 m resolution) satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. An Alaska FT map for April, 2007 was generated from PALSAR observations and showed a regionally consistent, but finer FT spatial pattern than an alternative surface air temperature based classification derived from global reanalysis data. Validation of the STA based FT classification against regional soil climate stations indicated approximately 80% and 70% spatial classification accuracy in relation to respective in situ station air temperature and soil temperature measurement based FT estimates. The STA FT classification method is found to be reliable for most of the major Alaska land cover types except for barren land. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between pixel size and relative FT spatial classification error follows a general logarithmic function. The optimum resolution for accurate FT classification is expected to depend on the landscape FT spatial heterogeneity. However, our results indicate that the regional FT spatial scaling error is less than 12.8% and

  12. An Intercomparison Between Radar Reflectivity and the IR Cloud Classification Technique for the TOGA-COARE Area

    NASA Technical Reports Server (NTRS)

    Carvalho, L. M. V.; Rickenbach, T.

    1999-01-01

    Satellite infrared (IR) and visible (VIS) images from the Tropical Ocean Global Atmosphere - Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) experiment are investigated through the use of Clustering Analysis. The clusters are obtained from the values of IR and VIS counts and the local variance for both channels. The clustering procedure is based on the standardized histogram of each variable obtained from 179 pairs of images. A new approach to classify high clouds using only IR and the clustering technique is proposed. This method allows the separation of the enhanced convection in two main classes: convective tops, more closely related to the most active core of the storm, and convective systems, which produce regions of merged, thick anvil clouds. The resulting classification of different portions of cloudiness is compared to the radar reflectivity field for intensive events. Convective Systems and Convective Tops are followed during their life cycle using the IR clustering method. The areal coverage of precipitation and features related to convective and stratiform rain is obtained from the radar for each stage of the evolving Mesoscale Convective Systems (MCS). In order to compare the IR clustering method with a simple threshold technique, two IR thresholds (Tir) were used to identify different portions of cloudiness, Tir=240K which roughly defines the extent of all cloudiness associated with the MCS, and Tir=220K which indicates the presence of deep convection. It is shown that the IR clustering technique can be used as a simple alternative to identify the actual portion of convective and stratiform rainfall.

  13. Automatic Focusing for a 675 GHz Imaging Radar with Target Standoff Distances from 14 to 34 Meters

    NASA Technical Reports Server (NTRS)

    Tang, Adrian; Cooper, Ken B.; Dengler, Robert J.; Llombart, Nuria; Siegel, Peter H.

    2013-01-01

    This paper dicusses the issue of limited focal depth for high-resolution imaging radar operating over a wide range of standoff distances. We describe a technique for automatically focusing a THz imaging radar system using translational optics combined with range estimation based on a reduced chirp bandwidth setting. The demonstarted focusing algorithm estimates the correct focal depth for desired targets in the field of view at unknown standoffs and in the presence of clutter to provide good imagery at 14 to 30 meters of standoff.

  14. Spectral analysis of ground penetrating radar signals in concrete, metallic and plastic targets

    NASA Astrophysics Data System (ADS)

    Santos, Vinicius Rafael N. dos; Al-Nuaimy, Waleed; Porsani, Jorge Luís; Hirata, Nina S. Tomita; Alzubi, Hamzah S.

    2014-01-01

    The accuracy of detecting buried targets using ground penetrating radar (GPR) depends mainly on features that are extracted from the data. The objective of this study is to test three spectral features and evaluate the quality to provide a good discrimination among three types of materials (concrete, metallic and plastic) using the 200 MHz GPR system. The spectral features which were selected to check the interaction of the electromagnetic wave with the type of material are: the power spectral density (PSD), short-time Fourier transform (STFT) and the Wigner-Ville distribution (WVD). The analyses were performed with simulated data varying the sizes of the targets and the electrical properties (relative dielectric permittivity and electrical conductivity) of the soil. To check if the simulated data are in accordance with the real data, the same approach was applied on the data obtained in the IAG/USP test site. A noticeable difference was found in the amplitude of the studies' features in the frequency domain and these results show the strength of the signal processing to try to differentiate buried materials using GPR, and so can be used in urban planning and geotechnical studies.

  15. Per-point and per-field contextual classification of multipolarization and multiple incidence angle aircraft L-band radar data

    NASA Technical Reports Server (NTRS)

    Hoffer, Roger M.; Hussin, Yousif Ali

    1989-01-01

    Multipolarized aircraft L-band radar data are classified using two different image classification algorithms: (1) a per-point classifier, and (2) a contextual, or per-field, classifier. Due to the distinct variations in radar backscatter as a function of incidence angle, the data are stratified into three incidence-angle groupings, and training and test data are defined for each stratum. A low-pass digital mean filter with varied window size (i.e., 3x3, 5x5, and 7x7 pixels) is applied to the data prior to the classification. A predominately forested area in northern Florida was the study site. The results obtained by using these image classifiers are then presented and discussed.

  16. Coherence-based land cover classification in forested areas of Chattisgarh, Central India, using environmental satellite--advanced synthetic aperture radar data

    NASA Astrophysics Data System (ADS)

    Nizalapur, Vyjayanthi; Madugundu, Rangaswamy; Jha, Chandra Shekhar

    2011-01-01

    In the present work, the potential of synthetic aperture radar (SAR) interferometric coherence in land cover classification is studied over forested areas of Bilaspur, Chattisgarh, India using Environmental Satellite--Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data. Single look complex (SLC) interferometric pair ASAR data of 24th September 2006 (SLC-1) and 29th October 2006 (SLC-2) covering the study area were acquired and processed to generate backscatter and interferometric coherence images. A false colored composite of coherence, backscatter difference, and mean backscatter was generated and subjected to maximum likelihood classification to delineate major land cover classes of the study area viz., water, barren, agriculture, moist deciduous forest, and sal mixed forests. Accuracy assessment of the classified map is carried out using kappa statistics. Results of the study suggested potential use of ENVISAT-ASAR C-band data in land cover classification of the study area with an overall classification accuracy of 82.5%, average producer's accuracy of 83.69%, and average user's accuracy of 81%. The present study gives a unique scope of SAR data application in land cover classification over the tropical deciduous forest systems of India, which is still waiting for its indigenous SAR system.

  17. Planetary Radar

    NASA Technical Reports Server (NTRS)

    Neish, Catherine D.; Carter, Lynn M.

    2015-01-01

    This chapter describes the principles of planetary radar, and the primary scientific discoveries that have been made using this technique. The chapter starts by describing the different types of radar systems and how they are used to acquire images and accurate topography of planetary surfaces and probe their subsurface structure. It then explains how these products can be used to understand the properties of the target being investigated. Several examples of discoveries made with planetary radar are then summarized, covering solar system objects from Mercury to Saturn. Finally, opportunities for future discoveries in planetary radar are outlined and discussed.

  18. Domain definition and target classification for CASP6.

    PubMed

    Tress, Michael; Tai, Chin-Hsien; Wang, Guoli; Ezkurdia, Iakes; López, Gonzalo; Valencia, Alfonso; Lee, Byungkook; Dunbrack, Roland L

    2005-01-01

    Assessment of structure predictions in CASP6 was based on single domains isolated from experimentally determined structures, which were categorized into comparative modeling, fold recognition, and new fold targets. Domain definitions were defined upon visual examination of the structures with the aid of automated domain-parsing programs. Domain categorization was determined by comparison of the target structures with those in the Protein Data Bank at the time each target expired and a variety of sequence and structure-based methods to determine potential homologous relationships. PMID:16187342

  19. Full-polarization radar remote sensing and data mining for tropical crops mapping: a successful SVM-based classification model

    NASA Astrophysics Data System (ADS)

    Denize, J.; Corgne, S.; Todoroff, P.; LE Mezo, L.

    2015-12-01

    In Reunion, a tropical island of 2,512 km², 700 km east of Madagascar in the Indian Ocean, constrained by a rugged relief, agricultural sectors are competing in highly fragmented agricultural land constituted by heterogeneous farming systems from corporate to small-scale farming. Policymakers, planners and institutions are in dire need of reliable and updated land use references. Actually conventional land use mapping methods are inefficient under the tropic with frequent cloud cover and loosely synchronous vegetative cycles of the crops due to a constant temperature. This study aims to provide an appropriate method for the identification and mapping of tropical crops by remote sensing. For this purpose, we assess the potential of polarimetric SAR imagery associated with associated with machine learning algorithms. The method has been developed and tested on a study area of 25*25 km thanks to 6 RADARSAT-2 images in 2014 in full-polarization. A set of radar indicators (backscatter coefficient, bands ratios, indices, polarimetric decompositions (Freeman-Durden, Van zyl, Yamaguchi, Cloude and Pottier, Krogager), texture, etc.) was calculated from the coherency matrix. A random forest procedure allowed the selection of the most important variables on each images to reduce the dimension of the dataset and the processing time. Support Vector Machines (SVM), allowed the classification of these indicators based on a learning database created from field observations in 2013. The method shows an overall accuracy of 88% with a Kappa index of 0.82 for the identification of four major crops.

  20. Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields.

    PubMed

    Fischell, Erin M; Schmidt, Henrik

    2015-12-01

    One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7-9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834-875 (2010)]. PMID:26723332

  1. Passive position-adaptive radar modes for non-LOS interrogation of embedded targets

    NASA Astrophysics Data System (ADS)

    Mitra, Atindra K.

    2004-08-01

    A position-adaptive radar system concept is presented for purposes of interrogating difficult and obscured targets via the application of low-altitude smart or robotic-type UAV platforms. Under this concept, a high-altitude radiating platform is denoted as a HUAV and a low-altitude "position-adaptive" platform is denoted as a LUAV. The system concept is described by two modes. In Mode-1, real-time onboard LUAV computation of a phase parameter denoted as "signal differential path length" allows the LUAV to position-adaptively isolate a "signal leakage point", for example, between two buildings. After the LUAV position-adaptively converges to an optimum location, the system enters Mode-2. Under this Mode-2 concept, a technique denoted as "exploitation of leakage signals via path trajectory diversity" (E-LS-PTD) is developed. This technique is based on modulating scattering centers on embedded objects by implementing a fast trajectory on the HUAV while the LUAV is hovering in front of an "obscuration channel." Analytical results include sample outputs from an initial set numerical electromagnetic simulations.

  2. Classification

    NASA Astrophysics Data System (ADS)

    Oza, Nikunj

    2012-03-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example

  3. Three-dimensional analysis of moving target radar signals: methods and implications for ATR and feature-aided tracking

    NASA Astrophysics Data System (ADS)

    Stuff, Mark A.

    1999-08-01

    Like the hypothetical shadow watchers of Plato's cave, ATR researchers have spent years in the study of one and two- dimensional signals, collected from three dimensional targets. Three-dimensional geometric invariance theory of radar returns from moving targets gives us a new opportunity to escape the study of two-dimensional information which is present, with probability one, in the signals from any randomly moving target. Target recognition for moving targets is fundamentally harder than for stationary targets, if one remains in a two- dimensional paradigm. Viewing geometry calculations based on sensor flight lines become false, due to uncontrolled target rotations. Three-dimensional analysis shows that even the most optimal purely two-dimensional approach will generically construct false target measurements and distorted target images. But the geometric facts also show that all types of three-dimensional Euclidean invariants, such as true (not projected) lengths, surface areas, angles, and volumes of target components can be extracted from moving target data. These facts have profound implications for target recognition, and for the dynamic tracking of target movements, allowing target signals to be correlated by comparing fundamental three-dimensional invariants, which are not confounded by changing illumination directions.

  4. Road-Aided Ground Slowly Moving Target 2D Motion Estimation for Single-Channel Synthetic Aperture Radar.

    PubMed

    Wang, Zhirui; Xu, Jia; Huang, Zuzhen; Zhang, Xudong; Xia, Xiang-Gen; Long, Teng; Bao, Qian

    2016-01-01

    To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method. The target's position on the road as well as its radial velocity can be determined according to the target's offset distance and traffic rules. Furthermore, the target's azimuth velocity is estimated based on the road slope obtained via polynomial fitting. Compared with the traditional algorithms, the proposed method can effectively cope with slowly moving targets partly submerged in a stationary clutter spectrum. In addition, the proposed method can be easily extended to a multi-channel system to further improve the performance of clutter suppression and motion estimation. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm. PMID:26999140

  5. KNN classification of metallic targets using the magnetic polarizability tensor

    NASA Astrophysics Data System (ADS)

    Makkonen, J.; Marsh, L. A.; Vihonen, J.; Järvi, A.; Armitage, D. W.; Visa, A.; Peyton, A. J.

    2014-05-01

    Walk-through metal detectors are used at check points for preventing personnel and passengers from carrying threatening metallic objects, such as knives and guns, into a secure area. These systems are capable of detecting small metallic items, such as handcuff keys and blades, but are unable to distinguish accurately between threatening objects and innocuous items. This paper studies the extent to which a K-nearest-neighbour classifier can distinguish various kinds of metallic objects, such as knives, shoe shanks, belts and containers. The classifier uses features extracted from the magnetic polarizability tensor, which represents the electromagnetic properties of the object. The tests include distinguishing threatening objects from innocuous ones, classifying a set of objects into 13 classes, and distinguishing between several similar objects within an object class. A walk-through metal detection system is used as source for the test data, which consist of 835 scans and 67 objects. The results presented show a typical success rate of over 95% for recognizing threats, and over 85% for correct classification. In addition, we have shown that the system is capable of distinguishing between similar objects reliably. Overall, the method shows promise for the field of security screening and suggests the need for further research.

  6. Integration of a road network into a radar ground moving target tracking (GMTT) system and its performance evaluation

    NASA Astrophysics Data System (ADS)

    Blackman, Sam; Fong, Kathy; Carroll, Douglas E.; Lancaster, Justin; Dempster, Robert

    2009-08-01

    This paper discusses the application of multiple hypothesis tracking (MHT) to the processing of ground target data collected with a long range surveillance radar. A key element in the successful tracking of ground targets is the use of road networks. Thus, the paper begins with an overview of the alternative approaches that have been considered for incorporating road data into a ground target tracker and then it gives a detailed description of the methods that have been chosen. The major design issues to be addressed include the manner in which road filter models are included into a Variable-Structure Interacting Multiple Model (IMM) filtering scheme, how the road filter models are chosen to handle winding roads and intersections, and the tracking of targets that go on and off-road. Performance will be illustrated using simulated data and real data collected from a large surveillance area with a GMTI radar. The area considered contains regions of heavy to moderate target densities and clutter. Since the real data included only targets of opportunity (TOO), it was necessary to define metrics to evaluate relative performance as alternative tracking methods/parameters are considered. These metrics are discussed and comparative results are presented.

  7. Misclassification Errors in Unsupervised Classification Methods. Comparison Based on the Simulation of Targeted Proteomics Data

    PubMed Central

    Andreev, Victor P; Gillespie, Brenda W; Helfand, Brian T; Merion, Robert M

    2016-01-01

    Unsupervised classification methods are gaining acceptance in omics studies of complex common diseases, which are often vaguely defined and are likely the collections of disease subtypes. Unsupervised classification based on the molecular signatures identified in omics studies have the potential to reflect molecular mechanisms of the subtypes of the disease and to lead to more targeted and successful interventions for the identified subtypes. Multiple classification algorithms exist but none is ideal for all types of data. Importantly, there are no established methods to estimate sample size in unsupervised classification (unlike power analysis in hypothesis testing). Therefore, we developed a simulation approach allowing comparison of misclassification errors and estimating the required sample size for a given effect size, number, and correlation matrix of the differentially abundant proteins in targeted proteomics studies. All the experiments were performed in silico. The simulated data imitated the expected one from the study of the plasma of patients with lower urinary tract dysfunction with the aptamer proteomics assay Somascan (SomaLogic Inc, Boulder, CO), which targeted 1129 proteins, including 330 involved in inflammation, 180 in stress response, 80 in aging, etc. Three popular clustering methods (hierarchical, k-means, and k-medoids) were compared. K-means clustering performed much better for the simulated data than the other two methods and enabled classification with misclassification error below 5% in the simulated cohort of 100 patients based on the molecular signatures of 40 differentially abundant proteins (effect size 1.5) from among the 1129-protein panel. PMID:27524871

  8. Signal processing techniques for surveillance radar - An overview

    NASA Astrophysics Data System (ADS)

    Farina, A.; Galati, G.

    1985-06-01

    The present paper is concerned with a survey of the signal processing techniques presently employed in modern air defense and surveillance radars and those techniques likely to be applied in the future. Attention is given to the requirements for enhancing performance in surveillance radar, current processing techniques, advanced techniques, low probability of intercept (LPI) and anti-ARM (anti-radiation missile), anti-stealth, digital beamforming (DBF), adaptivity, high directivity and high resolution, multidimensional processing, target classification, and fieldability. Stealth is the term given to means of reducing the radar cross section of a target and the reduction of infrared emissions from the engine exhaust.

  9. Absolute radiometric calibration of Als intensity data: effects on accuracy and target classification.

    PubMed

    Kaasalainen, Sanna; Pyysalo, Ulla; Krooks, Anssi; Vain, Ants; Kukko, Antero; Hyyppä, Juha; Kaasalainen, Mikko

    2011-01-01

    Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data. PMID:22346660

  10. Absolute Radiometric Calibration of ALS Intensity Data: Effects on Accuracy and Target Classification

    PubMed Central

    Kaasalainen, Sanna; Pyysalo, Ulla; Krooks, Anssi; Vain, Ants; Kukko, Antero; Hyyppä, Juha; Kaasalainen, Mikko

    2011-01-01

    Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data. PMID:22346660

  11. Seismic target classification using a wavelet packet manifold in unattended ground sensors systems.

    PubMed

    Huang, Jingchang; Zhou, Qianwei; Zhang, Xin; Song, Enliang; Li, Baoqing; Yuan, Xiaobing

    2013-01-01

    One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS) systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM), by addressing the neighborhood preserving embedding (NPE) algorithm of manifold learning on the wavelet packet node energy (WPNE) of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC) is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity. PMID:23881125

  12. Performance Evaluation of Target Detection with a Near-Space Vehicle-Borne Radar in Blackout Condition.

    PubMed

    Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Deng, Bin; Qin, Yuliang

    2016-01-01

    Radar is a very important sensor in surveillance applications. Near-space vehicle-borne radar (NSVBR) is a novel installation of a radar system, which offers many benefits, like being highly suited to the remote sensing of extremely large areas, having a rapidly deployable capability and having low vulnerability to electronic countermeasures. Unfortunately, a target detection challenge arises because of complicated scenarios, such as nuclear blackout, rain attenuation, etc. In these cases, extra care is needed to evaluate the detection performance in blackout situations, since this a classical problem along with the application of an NSVBR. However, the existing evaluation measures are the probability of detection and the receiver operating curve (ROC), which cannot offer detailed information in such a complicated application. This work focuses on such requirements. We first investigate the effect of blackout on an electromagnetic wave. Performance evaluation indexes are then built: three evaluation indexes on the detection capability and two evaluation indexes on the robustness of the detection process. Simulation results show that the proposed measure will offer information on the detailed performance of detection. These measures are therefore very useful in detecting the target of interest in a remote sensing system and are helpful for both the NSVBR designers and users. PMID:26751445

  13. Performance Evaluation of Target Detection with a Near-Space Vehicle-Borne Radar in Blackout Condition

    PubMed Central

    Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Deng, Bin; Qin, Yuliang

    2016-01-01

    Radar is a very important sensor in surveillance applications. Near-space vehicle-borne radar (NSVBR) is a novel installation of a radar system, which offers many benefits, like being highly suited to the remote sensing of extremely large areas, having a rapidly deployable capability and having low vulnerability to electronic countermeasures. Unfortunately, a target detection challenge arises because of complicated scenarios, such as nuclear blackout, rain attenuation, etc. In these cases, extra care is needed to evaluate the detection performance in blackout situations, since this a classical problem along with the application of an NSVBR. However, the existing evaluation measures are the probability of detection and the receiver operating curve (ROC), which cannot offer detailed information in such a complicated application. This work focuses on such requirements. We first investigate the effect of blackout on an electromagnetic wave. Performance evaluation indexes are then built: three evaluation indexes on the detection capability and two evaluation indexes on the robustness of the detection process. Simulation results show that the proposed measure will offer information on the detailed performance of detection. These measures are therefore very useful in detecting the target of interest in a remote sensing system and are helpful for both the NSVBR designers and users. PMID:26751445

  14. Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition

    NASA Astrophysics Data System (ADS)

    Ding, Baiyuan; Wen, Gongjian; Zhong, Jinrong; Ma, Conghui; Yang, Xiaoliang

    2016-01-01

    This paper proposes a robust method for the matching of attributed scattering centers (ASCs) with application to synthetic aperture radar automatic target recognition (ATR). For the testing image to be classified, ASCs are extracted to match with the ones predicted by templates. First, Hungarian algorithm is employed to match those two ASC sets initially. Then, a precise matching is carried out through a threshold method. Point similarity and structure similarity are calculated, which are fused to evaluate the overall similarity of the two ASC sets based on the Dempster-Shafer theory of evidence. Finally, the target type is determined by such similarities between the testing image and various types of targets. Experiments on the moving and stationary target acquisition and recognition data verify the validity of the proposed method.

  15. Information theoretic bounds of ATR algorithm performance for sidescan sonar target classification

    NASA Astrophysics Data System (ADS)

    Myers, Vincent L.; Pinto, Marc A.

    2005-05-01

    With research on autonomous underwater vehicles for minehunting beginning to focus on cooperative and adaptive behaviours, some effort is being spent on developing automatic target recognition (ATR) algorithms that are able to operate with high reliability under a wide range of scenarios, particularly in areas of high clutter density, and without human supervision. Because of the great diversity of pattern recognition methods and continuously improving sensor technology, there is an acute requirement for objective performance measures that are independent of any particular sensor, algorithm or target definitions. This paper approaches the ATR problem from the point of view of information theory in an attempt to place bounds on the performance of target classification algorithms that are based on the acoustic shadow of proud targets. Performance is bounded by analysing the simplest of shape classification tasks, that of differentiating between a circular and square shadow, thus allowing us to isolate system design criteria and assess their effect on the overall probability of classification. The information that can be used for target recognition in sidescan sonar imagery is examined and common information theory relationships are used to derive properties of the ATR problem. Some common bounds with analytical solutions are also derived.

  16. Measuring elimination of podoconiosis, endemicity classifications, case definition and targets: an international Delphi exercise

    PubMed Central

    Deribe, Kebede; Wanji, Samuel; Shafi, Oumer; Muheki Tukahebwa, Edridah; Umulisa, Irenee; Davey, Gail

    2015-01-01

    Background Podoconiosis is one of the major causes of lymphoedema in the tropics. Nonetheless, currently there are no endemicity classifications or elimination targets to monitor the effects of interventions. This study aimed at establishing case definitions and indicators that can be used to assess endemicity, elimination and clinical outcomes of podoconiosis. Methods This paper describes the result of a Delphi technique used among 28 experts. A questionnaire outlining possible case definitions, endemicity classifications, elimination targets and clinical outcomes was developed. The questionnaire was distributed to experts working on podoconiosis and other neglected tropical diseases in two rounds. The experts rated the importance of case definitions, endemic classifications, elimination targets and the clinical outcome measures. Median and mode were used to describe the central tendency of expert responses. The coefficient of variation was used to describe the dispersals of expert responses. Results Consensus on definitions and indicators for assessing endemicity, elimination and clinical outcomes of podoconiosis directed at policy makers and health workers was achieved following the two rounds of Delphi approach among the experts. Conclusions Based on the two Delphi rounds we discuss potential indicators and endemicity classification of this disabling disease, and the ongoing challenges to its elimination in countries with the highest prevalence. Consensus will help to increase effectiveness of podoconiosis elimination efforts and ensure comparability of outcome data. PMID:26185194

  17. Application of Cloude's target decomposition theorem to polarimetric imaging radar data

    NASA Technical Reports Server (NTRS)

    Vanzyl, Jakob J.

    1993-01-01

    In this paper we applied Cloude's decomposition to imaging radar polarimetry. We show in detail how the decomposition results can guide the interpretation of scattering from vegetated areas. For multifrequency polarimetric radar measurements of a clear-cut area, the decomposition leads us to conclude that the vegetation is probably thin compared to even the C-band radar wavelength of 6 cm. For a frosted area, we notice an increased amount of even number of reflection scattering at P-band and L-band, probably the result of penetration through the coniferous canopy resulting in trunk-ground double reflection scattering. However, the scattering for the forested area is still dominated by scattering from randomly oriented cylinders. It is found that these cylinders are thicker than in the case of clear-cut areas, leading us to conclude that scattering from the branches probably dominates in this case.

  18. Road-Aided Ground Slowly Moving Target 2D Motion Estimation for Single-Channel Synthetic Aperture Radar

    PubMed Central

    Wang, Zhirui; Xu, Jia; Huang, Zuzhen; Zhang, Xudong; Xia, Xiang-Gen; Long, Teng; Bao, Qian

    2016-01-01

    To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method. The target’s position on the road as well as its radial velocity can be determined according to the target’s offset distance and traffic rules. Furthermore, the target’s azimuth velocity is estimated based on the road slope obtained via polynomial fitting. Compared with the traditional algorithms, the proposed method can effectively cope with slowly moving targets partly submerged in a stationary clutter spectrum. In addition, the proposed method can be easily extended to a multi-channel system to further improve the performance of clutter suppression and motion estimation. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm. PMID:26999140

  19. Multiple target tracking and classification improvement using data fusion at node level using acoustic signals

    NASA Astrophysics Data System (ADS)

    Damarla, T. R.; Whipps, Gene

    2005-05-01

    Target tracking and classification using passive acoustic signals is difficult at best as the signals are contaminated by wind noise, multi-path effects, road conditions, and are generally not deterministic. In addition, microphone characteristics, such as sensitivity, vary with the weather conditions. The problem is further compounded if there are multiple targets, especially if some are measured with higher signal-to-noise ratios (SNRs) than the others and they share spectral information. At the U. S. Army Research Laboratory we have conducted several field experiments with a convoy of two, three, four and five vehicles traveling on different road surfaces, namely gravel, asphalt, and dirt roads. The largest convoy is comprised of two tracked vehicles and three wheeled vehicles. Two of the wheeled vehicles are heavy trucks and one is a light vehicle. We used a super-resolution direction-of-arrival estimator, specifically the minimum variance distortionless response, to compute the bearings of the targets. In order to classify the targets, we modeled the acoustic signals emanated from the targets as a set of coupled harmonics, which are related to the engine-firing rate, and subsequently used a multivariate Gaussian classifier. Independent of the classifier, we find tracking of wheeled vehicles to be intermittent as the signals from vehicles with high SNR dominate the much quieter wheeled vehicles. We used several fusion techniques to combine tracking and classification results to improve final tracking and classification estimates. We will present the improvements (or losses) made in tracking and classification of all targets. Although improvements in the estimates for tracked vehicles are not noteworthy, significant improvements are seen in the case of wheeled vehicles. We will present the fusion algorithm used.

  20. Moving target exploitation

    NASA Astrophysics Data System (ADS)

    Johnson, Bruce L.; Grayson, Timothy P.

    1998-08-01

    The understanding of maneuvering forces is invaluable to the warfighter, as it enhances understanding of enemy force structure and disposition, provides cues to potential enemy actions, and expedites targeting of time critical targets. Airborne ground moving target indicator (GMTI) radars are a class of highly-effective, all-weather, wide-area senors that aid in the surveillance of these moving ground vehicles. Unfortunately conventional GMTI radars are incapable of identifying individual vehicles, and techniques for exploiting information imbedded within GMTI radar reports are limited. The Defense Advanced Research Projects Agency (DARPA) Moving Target Exploitation (MTE) program is working to mitigate these deficiencies by developing, integrating, and evaluating a suite of automated and semi-automated technologies to classify moving targets and units, and to provide indications of their activities. These techniques include: aid in the interpretation of GMTI data to provide moving force structure analysis, automatic tracking of thousands of moving ground vehicles, 1-D target classification based upon high-range- resolution (HRR) radar profiles, and 2-D target classification based upon moving target imaging (MTIm) synthetic aperture radar (SAR). This paper shall present the MTE concept and motivation and provide an overview of results to date.

  1. Application of SEASAT-1 Synthetic Aperture Radar (SAR) data to enhance and detect geological lineaments and to assist LANDSAT landcover classification mapping. [Appalachian Region, West Virginia

    NASA Technical Reports Server (NTRS)

    Sekhon, R.

    1981-01-01

    Digital SEASAT-1 synthetic aperture radar (SAR) data were used to enhance linear features to extract geologically significant lineaments in the Appalachian region. Comparison of Lineaments thus mapped with an existing lineament map based on LANDSAT MSS images shows that appropriately processed SEASAT-1 SAR data can significantly improve the detection of lineaments. Merge MSS and SAR data sets were more useful fo lineament detection and landcover classification than LANDSAT or SEASAT data alone. About 20 percent of the lineaments plotted from the SEASAT SAR image did not appear on the LANDSAT image. About 6 percent of minor lineaments or parts of lineaments present in the LANDSAT map were missing from the SEASAT map. Improvement in the landcover classification (acreage and spatial estimation accuracy) was attained by using MSS-SAR merged data. The aerial estimation of residential/built-up and forest categories was improved. Accuracy in estimating the agricultural and water categories was slightly reduced.

  2. Performance analysis of spectrally versatile forward-looking ground-penetrating radar for detection of concealed targets

    NASA Astrophysics Data System (ADS)

    Phelan, Brian R.; Ressler, Marc A.; Ranney, Kenneth I.; Smith, Gregory D.; Kirose, Getachew A.; Sherbondy, Kelly D.; Narayanan, Ram M.

    2015-05-01

    Stepped-Frequency Radars (SFRs) have become increasingly popular with the advent of new technologies and increasingly congested RF spectrum. SFRs have inherently high dynamic range due to their small IF bandwidths, allowing for the detection of weak target returns in the presence of clutter. The Army Research Laboratory's (ARL) Partnership in Research Transition program has developed a preliminary SFR for imaging buried landmines and improvised explosive devices. The preliminary system utilizes two transmit antennas and four receive antennas and is meant to act as a transitional system to verify the system's design and imaging capabilities. The SFR operates between 300 MHz and 2000 MHz, and is capable of 1-MHz step-sizes. The SFR system will eventually utilize 16-receive channels and will be mounted on ARL's existing Forward-Looking Ground Penetrating Radar platform, as a replacement for the existing Synchronous Impulse REconstruction (SIRE) radar. An analysis of the preliminary SFRs radio frequency interference mitigation, spectral purity dynamic range, and maximum detectable range is presented here.

  3. Classification

    ERIC Educational Resources Information Center

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  4. Non-cooperative target recognition by means of singular value decomposition applied to radar high resolution range profiles.

    PubMed

    López-Rodríguez, Patricia; Escot-Bocanegra, David; Fernández-Recio, Raúl; Bravo, Ignacio

    2015-01-01

    Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising. PMID:25551484

  5. Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles †

    PubMed Central

    López-Rodríguez, Patricia; Escot-Bocanegra, David; Fernández-Recio, Raúl; Bravo, Ignacio

    2015-01-01

    Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising. PMID:25551484

  6. Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

    PubMed Central

    Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

    2007-01-01

    The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability.

  7. Detection and Classification of Finer-Grained Human Activities Based on Stepped-Frequency Continuous-Wave Through-Wall Radar

    PubMed Central

    Qi, Fugui; Liang, Fulai; Lv, Hao; Li, Chuantao; Chen, Fuming; Wang, Jianqi

    2016-01-01

    The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method. PMID:27314362

  8. Detection and Classification of Finer-Grained Human Activities Based on Stepped-Frequency Continuous-Wave Through-Wall Radar.

    PubMed

    Qi, Fugui; Liang, Fulai; Lv, Hao; Li, Chuantao; Chen, Fuming; Wang, Jianqi

    2016-01-01

    The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method. PMID:27314362

  9. Dielectrophoresis-based classification of cells using multi-target multiple-hypothesis tracking.

    PubMed

    Dickerson, Samuel J; Chiarulli, Donald M; Levitan, Steven P; Carthel, Craig; Coraluppi, Stefano

    2014-01-01

    In this paper we present a novel methodology for classifying cells by using a combination of dielectrophoresis, image tracking and classification algorithms. We use dielectrophoresis to induce unique motion patterns in cells of interest. Motion is extracted via multi-target multiple-hypothesis tracking. Trajectories are then used to classify cells based on a generalized likelihood ratio test. We present results of a simulation study and of our prototype tracking the dielectrophoretic velocities of cells. PMID:25570230

  10. Object-oriented fusion of RADARSAT-2 polarimetric synthetic aperture radar and HJ-1A multispectral data for land-cover classification

    NASA Astrophysics Data System (ADS)

    Xiao, Yan; Jiang, Qigang; Wang, Bin; Li, Yuanhua; Liu, Shu; Cui, Can

    2016-04-01

    The contribution of the integration of optical and polarimetric synthetic aperture radar (PolSAR) data to accurate land-cover classification was investigated. For this purpose, an object-oriented classification methodology that consisted of polarimetric decomposition, hybrid feature selection, and a support vector machine (SVM) was proposed. A RADARSAT-2 Fine Quad-Pol image and an HJ-1A CCD2 multispectral image were used as data sources. First, polarimetric decomposition was implemented for the RADARSAT-2 image. Sixty-one polarimetric parameters were extracted using different polarimetric decomposition methods and then merged with the main diagonal elements (T11, T22, T33) of the coherency matrix to form a multichannel image with 64 layers. Second, the HJ-1A and the multichannel images were divided into numerous image objects by implementing multiresolution segmentation. Third, 1104 features were extracted from the HJ-1A and the multichannel images for each image object. Fourth, the hybrid feature selection method that combined the ReliefF filter approach and the genetic algorithm (GA) wrapper approach (ReliefF-GA) was used. Finally, land-cover classification was performed by an SVM classifier on the basis of the selected features. Five other classification methodologies were conducted for comparison to verify the contribution of optical and PolSAR data integration and to test the superiority of the proposed object-oriented classification methodology. Comparison results show that HJ-1A data, RADARSAT-2 data, polarimetric decomposition, ReliefF-GA, and SVM have a significant contribution by improving land-cover classification accuracy.

  11. A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging

    NASA Astrophysics Data System (ADS)

    Agarwal, Smriti; Bisht, Amit Singh; Singh, Dharmendra; Pathak, Nagendra Prasad

    2014-12-01

    Millimetre wave imaging (MMW) is gaining tremendous interest among researchers, which has potential applications for security check, standoff personal screening, automotive collision-avoidance, and lot more. Current state-of-art imaging techniques viz. microwave and X-ray imaging suffers from lower resolution and harmful ionizing radiation, respectively. In contrast, MMW imaging operates at lower power and is non-ionizing, hence, medically safe. Despite these favourable attributes, MMW imaging encounters various challenges as; still it is very less explored area and lacks suitable imaging methodology for extracting complete target information. Keeping in view of these challenges, a MMW active imaging radar system at 60 GHz was designed for standoff imaging application. A C-scan (horizontal and vertical scanning) methodology was developed that provides cross-range resolution of 8.59 mm. The paper further details a suitable target identification and classification methodology. For identification of regular shape targets: mean-standard deviation based segmentation technique was formulated and further validated using a different target shape. For classification: probability density function based target material discrimination methodology was proposed and further validated on different dataset. Lastly, a novel artificial neural network based scale and rotation invariant, image reconstruction methodology has been proposed to counter the distortions in the image caused due to noise, rotation or scale variations. The designed neural network once trained with sample images, automatically takes care of these deformations and successfully reconstructs the corrected image for the test targets. Techniques developed in this paper are tested and validated using four different regular shapes viz. rectangle, square, triangle and circle.

  12. Automotive radar

    NASA Astrophysics Data System (ADS)

    Rohling, Hermann

    2004-07-01

    Radar networks for automtovie short-range applications (up to 30m) based on powerful but inexpensive 24GHz high range resolution pulse or FMCW radar systems have been developed at the Technical University of Hamburg-Harburg. The described system has been integrated in to an experimental vehicle and tested in real street environment. This paper considers the general network design, the individual pulse or FMCW radar sensors, the network signal processing scheme, the tracking procedure and possible automotive applications, respectively. Object position estimation is accomplished by the very precise range measurement of each individual sensor and additional trilateration procedures. The paper concludes with some results obtained in realistic traffic conditions with multiple target situations using 24 GHz radar network.

  13. Application of Cloude's target decomposition theorem to polarimetric imaging radar data

    NASA Technical Reports Server (NTRS)

    Vanzyl, Jakob J.

    1993-01-01

    We apply Cloude's decomposition to imaging radar polarimetry. We derive the general expressions for the eigenvalues and eigenvectors for the case of terrain with reflection symmetry, and show in detail how the decomposition results can guide the interpretation of scattering from vegetated areas. For multi-frequency polarimetric radar measurements of a clear-cut area, the decomposition leads us to conclude that the vegetation is probably thin compared to even the C-band radar wavelength of 6 cm. For a forested area, we notice an increased amount of even number of reflection scattering at P-band and L-band, probably the result of penetration through the coniferous canopy resulting in trunk-ground double reflection scattering. The scattering for the forested area is still dominated by scattering from randomly oriented cylinders, however. It is found that these cylinders are thicker than in the case of clear-cut areas, leading us to conclude that scattering from the branches probably dominate in this case.

  14. Interferometric inverse synthetic aperture radar imaging for space targets based on wideband direct sampling using two antennas

    NASA Astrophysics Data System (ADS)

    Tian, Biao; Liu, Yang; Xu, Shiyou; Chen, Zengping

    2014-01-01

    Interferometric inverse synthetic aperture radar (InISAR) imaging provides complementary information to monostatic inverse synthetic aperture radar (ISAR) imaging. This paper proposes a new InISAR imaging system for space targets based on wideband direct sampling using two antennas. The system is easy to realize in engineering since the motion trajectory of space targets can be known in advance, which is simpler than that of three receivers. In the preprocessing step, high speed movement compensation is carried out by designing an adaptive matched filter containing speed that is obtained from the narrow band information. Then, the coherent processing and keystone transform for ISAR imaging are adopted to reserve the phase history of each antenna. Through appropriate collocation of the system, image registration and phase unwrapping can be avoided. Considering the situation not to be satisfied, the influence of baseline variance is analyzed and compensation method is adopted. The corresponding size can be achieved by interferometric processing of the two complex ISAR images. Experimental results prove the validity of the analysis and the three-dimensional imaging algorithm.

  15. Identification of human motion signature using airborne radar data

    NASA Astrophysics Data System (ADS)

    McDonald, Michael; Damini, Anthony

    2013-09-01

    Data containing the radar signature of amoving person on the groundwere collected at ranges of up to 30 kmfroma moving airborne platform using the DRDC Ottawa X-bandWideband Experimental Airborne Radar (XWEAR). The human target radar echo returns were found to possess a characteristic amplitude modulated (AM) and frequency modulated (FM) signature which could be usefully characterized in terms of conventional AM and FM modulation parameters. Human detection performance after space time adaptive processing is frequently limited by false alarms arising from incomplete cancellation of large radar cross-section discretes during the whitening step. However, the clutter discretes possess different modulation characteristics from the human targets discussed above. The ability of pattern classification techniques to use this parameter measurement space to distinguish between human targets and clutter discretes is explored and preliminary results presented.

  16. Parametric bicubic spline and CAD tools for complex targets shape modelling in physical optics radar cross section prediction

    NASA Astrophysics Data System (ADS)

    Delogu, A.; Furini, F.

    1991-09-01

    Increasing interest in radar cross section (RCS) reduction is placing new demands on theoretical, computation, and graphic techniques for calculating scattering properties of complex targets. In particular, computer codes capable of predicting the RCS of an entire aircraft at high frequency and of achieving RCS control with modest structural changes, are becoming of paramount importance in stealth design. A computer code, evaluating the RCS of arbitrary shaped metallic objects that are computer aided design (CAD) generated, and its validation with measurements carried out using ALENIA RCS test facilities are presented. The code, based on the physical optics method, is characterized by an efficient integration algorithm with error control, in order to contain the computer time within acceptable limits, and by an accurate parametric representation of the target surface in terms of bicubic splines.

  17. Depth classification of underwater targets based on complex acoustic intensity of normal modes

    NASA Astrophysics Data System (ADS)

    Yang, Guang; Yin, Jingwei; Yu, Yun; Shi, Zhenhua

    2016-04-01

    In order to solve the problem of depth classification of the underwater target in a very low frequency acoustic field, the active component of cross spectra of particle pressure and horizontal velocity (ACCSPPHV) is adopted to distinguish the surface vessel and the underwater target. According to the effective depth of a Pekeris waveguide, the placing depth forecasting equations of passive vertical double vector hydrophones are proposed. Numerical examples show that when the sum of depths of two hydrophones is the effective depth, the sign distribution of ACCSPPHV has nothing to do with horizontal distance; in addition, the sum of the first critical surface and the second critical surface is equal to the effective depth. By setting the first critical surface less than the difference between the effective water depth and the actual water depth, that is, the second critical surface is greater than the actual depth, the three positive and negative regions of the whole ocean volume are equivalent to two positive and negative regions and therefore the depth classification of the underwater target is obtained. Besides, when the 20 m water depth is taken as the first critical surface in the simulation of underwater targets (40 Hz, 50 Hz, and 60 Hz respectively), the effectiveness of the algorithm and the correctness of relevant conclusions are verified, and the analysis of the corresponding forecasting performance is conducted.

  18. Sensor Reliability Evaluation Scheme for Target Classification Using Belief Function Theory

    PubMed Central

    Zhu, Jing; Luo, Yupin; Zhou, Jianjun

    2013-01-01

    In the target classification based on belief function theory, sensor reliability evaluation has two basic issues: reasonable dissimilarity measure among evidences, and adaptive combination of static and dynamic discounting. One solution to the two issues has been proposed here. Firstly, an improved dissimilarity measure based on dualistic exponential function has been designed. We assess the static reliability from a training set by the local decision of each sensor and the dissimilarity measure among evidences. The dynamic reliability factors are obtained from each test target using the dissimilarity measure between the output information of each sensor and the consensus. Secondly, an adaptive combination method of static and dynamic discounting has been introduced. We adopt Parzen-window to estimate the matching degree of current performance and static performance for the sensor. Through fuzzy theory, the fusion system can realize self-learning and self-adapting with the sensor performance changing. Experiments conducted on real databases demonstrate that our proposed scheme performs better in target classification under different target conditions compared with other methods. PMID:24351632

  19. A new EMI system for detection and classification of challenging targets

    NASA Astrophysics Data System (ADS)

    Shubitidze, F.; Fernández, J. P.; Barrowes, B. E.; O'Neill, K.

    2013-06-01

    Advanced electromagnetic induction (EMI) sensors currently feature multi-axis illumination of targets and tri-axial vector sensing (e.g., MetalMapper), or exploit multi-static array data acquisition (e.g., TEMTADS). They produce data of high density, quality, and diversity, and have been combined with advanced EMI models to provide superb classification performance relative to the previous generation of single-axis, monostatic sensors. However, these advances yet have to improve significantly our ability to classify small, deep, and otherwise challenging targets. Particularly, recent live-site discrimination studies at Camp Butner, NC and Camp Beale, CA have revealed that it is more challenging to detect and discriminate small munitions (with calibers ranging from 20 mm to 60 mm) than larger ones. In addition, a live-site test at the Massachusetts Military Reservation, MA highlighted the difficulties for current sensors to classify large, deep, and overlapping targets with high confidence. There are two main approaches to overcome these problems: 1) adapt advanced EMI models to the existing systems and 2) improve the detection limits of current sensors by modifying their hardware. In this paper we demonstrate a combined software/hardware approach that will provide extended detection range and spatial resolution to next-generation EMI systems; we analyze and invert EMI data to extract classification features for small and deep targets; and we propose a new system that features a large transmitter coil.

  20. A computational theory for the classification of natural biosonar targets based on a spike code.

    PubMed

    Müller, Rolf

    2003-08-01

    A computational theory for the classification of natural biosonar targets is developed based on the properties of an example stimulus ensemble. An extensive set of echoes (84 800) from four different foliages was transcribed into a spike code using a parsimonious model (linear filtering, half-wave rectification, thresholding). The spike code is assumed to consist of time differences (interspike intervals) between threshold crossings. Among the elementary interspike intervals flanked by exceedances of adjacent thresholds, a few intervals triggered by disjoint half-cycles of the carrier oscillation stand out in terms of resolvability, visibility across resolution scales and a simple stochastic structure (uncorrelatedness). They are therefore argued to be a stochastic analogue to edges in vision. A three-dimensional feature vector representing these interspike intervals sustained a reliable target classification performance (0.06% classification error) in a sequential probability ratio test, which models sequential processing of echo trains by biological sonar systems. The dimensions of the representation are the first moments of duration and amplitude location of these interspike intervals as well as their number. All three quantities are readily reconciled with known principles of neural signal representation, since they correspond to the centre of gravity of excitation on a neural map and the total amount of excitation. PMID:12938773

  1. Contribution of Small-Scale Correlated Fluctuations of Microstructural Properties of a Spatially Extended Geophysical Target Under the Assessment of Radar Backscatter

    NASA Technical Reports Server (NTRS)

    Yurchak, Boris S.

    2010-01-01

    The study of the collective effects of radar scattering from an aggregation of discrete scatterers randomly distributed in a space is important for better understanding the origin of the backscatter from spatially extended geophysical targets (SEGT). We consider the microstructure irregularities of a SEGT as the essential factor that affect radar backscatter. To evaluate their contribution this study uses the "slice" approach: particles close to the front of incident radar wave are considered to reflect incident electromagnetic wave coherently. The radar equation for a SEGT is derived. The equation includes contributions to the total backscatter from correlated small-scale fluctuations of the slice's reflectivity. The correlation contribution changes in accordance with an earlier proposed idea by Smith (1964) based on physical consideration. The slice approach applied allows parameterizing the features of the SEGT's inhomogeneities.

  2. An accelerated framework for the classification of biological targets from solid-state micropore data.

    PubMed

    Hanif, Madiha; Hafeez, Abdul; Suleman, Yusuf; Mustafa Rafique, M; Butt, Ali R; Iqbal, Samir M

    2016-10-01

    Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis. PMID:27480732

  3. Three-Dimensional ISAR Imaging Method for High-Speed Targets in Short-Range Using Impulse Radar Based on SIMO Array.

    PubMed

    Zhou, Xinpeng; Wei, Guohua; Wu, Siliang; Wang, Dawei

    2016-01-01

    This paper proposes a three-dimensional inverse synthetic aperture radar (ISAR) imaging method for high-speed targets in short-range using an impulse radar. According to the requirements for high-speed target measurement in short-range, this paper establishes the single-input multiple-output (SIMO) antenna array, and further proposes a missile motion parameter estimation method based on impulse radar. By analyzing the motion geometry relationship of the warhead scattering center after translational compensation, this paper derives the receiving antenna position and the time delay after translational compensation, and thus overcomes the shortcomings of conventional translational compensation methods. By analyzing the motion characteristics of the missile, this paper estimates the missile's rotation angle and the rotation matrix by establishing a new coordinate system. Simulation results validate the performance of the proposed algorithm. PMID:26978372

  4. Three-Dimensional ISAR Imaging Method for High-Speed Targets in Short-Range Using Impulse Radar Based on SIMO Array

    PubMed Central

    Zhou, Xinpeng; Wei, Guohua; Wu, Siliang; Wang, Dawei

    2016-01-01

    This paper proposes a three-dimensional inverse synthetic aperture radar (ISAR) imaging method for high-speed targets in short-range using an impulse radar. According to the requirements for high-speed target measurement in short-range, this paper establishes the single-input multiple-output (SIMO) antenna array, and further proposes a missile motion parameter estimation method based on impulse radar. By analyzing the motion geometry relationship of the warhead scattering center after translational compensation, this paper derives the receiving antenna position and the time delay after translational compensation, and thus overcomes the shortcomings of conventional translational compensation methods. By analyzing the motion characteristics of the missile, this paper estimates the missile’s rotation angle and the rotation matrix by establishing a new coordinate system. Simulation results validate the performance of the proposed algorithm. PMID:26978372

  5. Estimating moving target information using single-channel synthetic aperture radar (SAR)

    NASA Astrophysics Data System (ADS)

    Gunther, Jacob; Hunsaker, Josh; Anderson, Hyrum; Moon, Todd

    2014-06-01

    Simultaneously estimating position x and velocity v of moving targets using only the measured phase ' from single-channel SAR is impossible because the mapping from (x, v) to φis many-to-one. This paper defines classes of equivalent target motion and solves the GMTI problem up to membership in an equivalence class using single-channel SAR phase data. Definitions are presented for endo- and exo-clutter that are consistent with the equivalence classes, and it is shown that most target motion can be detected, i.e. the set of endo-clutter targets is very small. We exploit the sparsity of moving targets in the scene to develop an algorithm to resolve target motion up to membership in an equivalence class, and demonstrate the effectiveness of the proposed technique using simulated data.

  6. Molecular Classification, Pathway Addiction, and Therapeutic Targeting in Diffuse Large B-cell Lymphoma

    PubMed Central

    Puvvada, Soham; Kendrick, Samantha; Rimsza, Lisa

    2015-01-01

    The rapid emergence of molecularly-based techniques to detect changes in the genetic landscape of diffuse large B-cell lymphoma (DLBCL) including gene expression, DNA and RNA sequencing, and epigenetic profiling, has significantly impacted the understanding and therapeutic targeting of DLBCL. In this review, we will briefly discuss the new methods used in the study of DLBCL. We will describe the influence of the generated data on DLBCL classification and the identification of new entities and altered cell survival strategies with a focus on the renewed interest in some classic oncogenic pathways that are currently targeted for new therapy. Lastly, we will examine the molecular genomic studies that revealed the importance of the tumor microenvironment in the pathogenesis of DLBCL. PMID:24080457

  7. Waveguide invariant active sonar target detection and depth classification in shallow water

    NASA Astrophysics Data System (ADS)

    Goldhahn, Ryan A.

    vital to maintaining low false alarm rates in active sonar. Moreover, because of the non-stationarity of the active sonar return, classification is most typically achieved using a single snapshot of test data. As an aid to classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide-invariant spectral density matrix (WI-SDM) which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is performed by a waveguide-invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) and averaging the signal replica vectors over the unknown channel parameters. Simulation and real data results from the SCARAB98, CLUTTER07, and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach.

  8. A targeted change-detection procedure by combining change vector analysis and post-classification approach

    NASA Astrophysics Data System (ADS)

    Ye, Su; Chen, Dongmei; Yu, Jie

    2016-04-01

    In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

  9. Micro-Doppler analysis of multiple frequency continuous wave radar signatures

    NASA Astrophysics Data System (ADS)

    Anderson, Michael G.; Rogers, Robert L.

    2007-04-01

    Micro-Doppler refers to Doppler scattering returns produced by non rigid-body motion. Micro-Doppler gives rise to many detailed radar image features in addition to those associated with bulk target motion. Targets of different classes (for example, humans, animals, and vehicles) produce micro-Doppler images that are often distinguishable even by nonexpert observers. Micro-Doppler features have great potential for use in automatic target classification algorithms. Although the potential benefit of using micro-Doppler in classification algorithms is high, relatively little experimental (non-synthetic) micro-Doppler data exists. Much of the existing experimental data comes from highly cooperative targets (human or vehicle targets directly approaching the radar). This research involved field data collection and analysis of micro-Doppler radar signatures from non-cooperative targets. The data was collected using a low cost Xband multiple frequency continuous wave (MFCW) radar with three transmit frequencies. The collected MFCW radar signatures contain data from humans, vehicles, and animals. The presented data includes micro-Doppler signatures previously unavailable in the literature such as crawling humans and various animal species. The animal micro-Doppler signatures include deer, dog, and goat datasets. This research focuses on the analysis of micro-Doppler from noncooperative targets approaching the radar at various angles, maneuvers, and postures.

  10. 1999 IEEE radar conference

    SciTech Connect

    1999-07-01

    This conference addresses the stringent radar technology demands facing the next century: target detection, tracking and identification; changing target environment; increased clutter mitigation techniques; air traffic control; transportation; drug smuggling; remote sensing, and other consumer oriented applications. A timely discussion covers how to minimize costs for these emerging areas. Advanced radar technology theory and applications are also presented. Topics covered include: signal processing; space time adaptive processing/antennas; surveillance technology; radar systems; dual use; and phenomenology.