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Joint detection and tracking of unresolved targets with a monopulse radar using a particle filter - art. no. 59131T

Joint detection and tracking of unresolved targets with a mo...

引用

17th conference on signal and data processing of small targets

作者： Nandakumaran, N Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)0819459186

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this paper, a new algorithm is developed to jointly detect and track more than two targets from a single detected bin. this method involves the use of tracking data in detection. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which is proved to be effective for nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. It in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. that is, we evaluate the likelihoods of different hypotheses of possible combinations of targets being in different detected bins. the hypothesis testing is used to find the correct detection event. the particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data with more than unresolved two targets. Simulation results confirm the possible extraction and tracking of more than two targets jointly.

关键词： U nresolved targets closely-spaced targets monopulse radar multitarget tracking particle filters

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Tracking multiple targets with coordinated turn maneuvers

Tracking multiple targets with coordinated turn maneuvers

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17th conference on signal and data processing of small targets

作者： Morelande, MR Gordon, NJ Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

the problem of tracking multiple maneuvering targets is considered. the usual multiple model approach is adopted in which maneuvering target motion is modeled by assuming that the target motion at each point in time can be described by one of a finite set of dynamic models. Transitions between each mode of target motion are assumed to be Markovian. Target positions are measured in polar coordinates leading to a nonlinear measurement equation. A particle filter is proposed as a solution to the problem. the proposed algorithm seeks to improve upon the performance of a previously proposed particle filter by using measurement-directed proposals and exploiting the structure of the measurement likelihood. the performance analysis focuses on targets which perform coordinated turn maneuvers. An improved model for target motion in this regime is suggested. the performance analysis, using Monte Carlo simulations, demonstrates the improved performance of the proposed algorithm compared to the previously proposed particle filter and the standard Gaussian approximation, the IMM-JPDAF.

关键词： Motion models Particles Detection and tracking algorithms Particle filters Monte Carlo methods Target detection Stochastic processes Sensors Electronic filtering data modeling

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MHT tracking for crossing sonar targets

MHT tracking for crossing sonar targets

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19th conference on signal and data processing of small targets

作者： Willett, Peter Luginbuhl, Tod Giannopoulos, Evangelos Univ Connecticut Dept ECE Storrs CT 06269 USA Naval Undersea Warfare Ctr Newport RI 02841 USA

ISBN: (纸本)9780819468475

Sometimes radar targets cross and become unresolved;this is a concern, but with a reasonable track depth and an appropriate merged-measurement model the concern is considerably mitigated. Sonar targets, however, can become merged (in the same beam) for considerably longer, particularly with bearing-only measurements. In such cases the crossing times can be 100 scans long, and no reasonable depth exists for an multi-frame tracker that can "see" both ends of the merged period. Further, there is a demonstrable tendency for estimated targets to repel each other as they are being tracked. In this paper we explore the hypothesis-oriented multi-hypothesis tracker (HO-MHT), an MHT approach that uses the new "rollout" optimization insight and the to give an appropriate and cost-effective means to rank hypotheses, and also the PMHT tracker that operates on batches of scans with linear computational complexity in most quantities. We show results in terms of estimation error (RMSE), consistency (NEES) and computational effort in both linear and beam-space tracking scenarios.

关键词： tracking MHT PMHT unresolved targets sonar

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Evaluation of an approximate crosscovariance calculation for track-to-track fusion

Evaluation of an approximate crosscovariance calculation for...

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16th conference on signal and data processing of small targets

作者： Sinav, A Balci, AM Bar-Shalom, Y Turkish Navy Res & Dev Ctr Command Istanbul Turkey

ISBN: (纸本)081945351X

the effect of the crosscovariance in track-to-track fusion has been studied in [2] [3] [4] [5]. Ignoring the crosscovariance of the local track estimation errors leads to optimistic covariance for the fused state estimates. However, evaluation of this crosscovariance is too demanding. Consequently, we approximate the crosscovariances between two sensors' track estimation errors in a manner similar to one of the procedures presented in [2], namely, with constant crosscorrelation coefficients. the results of using this in a practical naval surveillance system are discussed by comparing the exact covariances;of the fused tracks, the covariances obtained with the approximation and the covariances obtained by completely ignoring the crosscorrelations.

关键词： Error analysis Sensors Monte Carlo methods Filtering (signal processing) Matrices Radar Surveillance systems Computer engineering Detection and tracking algorithms Sensor fusion

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Aspect aware UAV localization

Aspect aware UAV localization

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20th conference on signal and data processing of small targets

作者： Berger, Christian R. Zhou, Shengli Willett, Peter Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819471604

We consider target detection and tracking of stealthy targets. these targets can be characterized by a, strong aspect dependence leading to difficult detectability without a multi-static setup. Even in a multi-static setup only sensors in a, certain zone can detect the return signal, if the the aspect dependent return has a small bandwidth. We propose a solution based on a large number of simple sensor, as using many receivers increases the probability of detection. the sensors are simple in the sense that they only transmit binary detection results to a fusion center that has comparatively deep capabilities, and they do not need to know their own position or communicate with other sensors. We characterize the target position estimation performance using the Cramer-Rao bound and simulation results, considering uncertainty in nuisance parameters as the sensor positions or the specifics of the aspect dependence. We suggest a data collection protocol that includes locating sensors that detect the target and has low communication complexity. As a, novelty we also include information about "non-localized" sensors, as sensors which do not detect the target stay quiet to save bandwidth and energy, therefore are not known to the fusion center except via knowledge of the deployed sensor density and deployment region.

关键词： Sensors Target detection signal detection Binary data Unmanned aerial vehicles Receivers Fusion energy signal to noise ratio Error analysis data communications

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Comparison of tracklet methods with deterministic dynamics and false signals - art. no. 62360Z

Comparison of tracklet methods with deterministic dynamics a...

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18th conference on signal and data processing of small targets

作者： Drummond, Oliver E. Dana-Bashian, David CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)0819462926

Track fusion processing is complicated because the estimation errors of a local track and a fusion track for the same target are usually cross-correlated. If these errors are cross-correlated, that should be taken into account when designing the data association processing and the filter used to combine the track data. An approach to dealing with this cross-correlation is to use tracklets. One of the important issues in tracklet fusion performance is whether the dynamics are deterministic, e.g., no filter process noise and no target maneuver. A number of different tracklet methods have been designed. this paper presents a comparison a tracklets-from-tracks approach to a tracklets-from-measurements approach. Tracklet fusion performance is also compared to centralized measurement fusion performance. the emphasis is on performance with targets that exhibit deterministic dynamics and the possibility of measurements caused by false signals.

关键词： target tracking multiple sensor tracking track fusion multiple target tracking sensor data fusion Bayesian methods tracklet distributed tracking estimation and filtering

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Simulation assessment of RCS-aided multiple target tracking

Simulation assessment of RCS-aided multiple target tracking

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19th conference on signal and data processing of small targets

作者： Dunham, Darin T. Ehrman, Lisa. M. Blair, W. Dale Frost, Susan A. Vectraxx Inc Glen Allen VA 23059 USA Georgia Inst Technol Sensors & Electromagnet Applicat Lab Georgia Tech Res Inst Atlanta GA 30332 USA Vectraxx Inc Ft Collins CO 80521 USA

ISBN: (纸本)9780819468475

Closely-spaced (but resolved) targets pose a significant challenge for single-frame unique measurement-to-track data association algorithms. this is due to the similarity of the Mahalanobis distances between the closely-spaced measurements and tracks. Contrary to conventional wisdom, adding target feature information (e.g target amplitude) does not necessarily improve the probability of correctly assigning measurements to tracks. In this paper, the theoretical limitations of using radar cross section (RCS) data to aid in measurement-to-track association are reviewed. the results of a hi,h-fidelity simulation assessment of the benefits of RCS-aided measurement-to-track association (using the signal-to-Noise Ratio) are given and other possibilities for RCS-aided tracking are discussed. Namely, we show the analytical results of our investigation into using RCS information to determine the presence of merged measurements.

关键词： signal to noise ratio Target detection Sensors Switches Kinematics Radar Mahalanobis distance signal processing Time metrology Detection and tracking algorithms

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Track fusion with feedback for local trackers using MHT

Track fusion with feedback for local trackers using MHT

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20th conference on signal and data processing of small targets

作者： Danu, Daniel Lang, Tom Kirubarajan, T. McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Gen Dynam Canada Ottawa ON Canada

ISBN: (纸本)9780819471604

With current processing power, Multiple Hypothesis Tracking (MHT) becomes a feasible and powerful solution;however a good hypothesis pruning method is mandatory for efficient implementation. the availability of a continuously increasing number of tracking systems raises interest in combining information from these systems. the purpose of this paper is to propose a method of information fusion for such trackers that use MHT locally with local information sent in the form of sensor global hypotheses and the fusion center combining them into fused global hypotheses. the information extracted from the best fused global hypotheses, in the form of ranking of received sensor global hypotheses, is sent back to local trackers, for optimized pruning. Details of the method, in terms of sensor global hypotheses generation, evaluation, pruning at local sensors, association and fusion of sensor global hypotheses at fusion center, and usage of the information received as feedback from the fusion center are presented.

关键词： target tracking Multiple Hypothesis Tracking data fusion

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Evaluation of a posteriori probabilities of multi-frame data association hypotheses

Evaluation of a posteriori probabilities of multi-frame data...

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19th conference on signal and data processing of small targets

作者： Mori, Shozo Chong, Chee BAE Syst Adv Informat Technol 5050 El Camino RealSuite 200 Los Altos CA 94022 USA

ISBN: (纸本)9780819468475

this paper discusses the problem of numerically evaluating multi-frame, data association hypotheses in multiple-target tracking in terms of their a posteriori probabilities. We describe two approaches to the problem: (1) an approach based on K-best multi-frame data association hypothesis selection algorithms, and (2) a more direct approach to calculating a posteriori probabilities through Markov-chain-Monte-Carlo (MCMC) or sequential Monte Carlo (SMC) methods. this paper defines algorithms based on those two approaches and compares their performance, and it discusses their relative effectiveness, using simple numerical examples.

关键词： multiple target tracking track-oriented multiple-hypothesis tracking (MHT) multiple-frame data association hypothesis K-best multiple-frame hypothesis selection Markov-chain-Monte-Carlo (MCMC) method sequential Monte Carlo (SMC) method

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A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking

A multiple model SNR/RCS likelihood ratio score for radar-ba...

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17th conference on signal and data processing of small targets

作者： Slocumb, BJ Klusman, ME Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

Most approaches to data association in target tracking use a likelihood-ratio based score for measurement-to-track and track-to-track matching. the classical approach uses a likelihood ratio based on kinematic data. Feature-aided tracking uses non-kinematic data to produce an "auxiliary score" that augments the kinematic score. this paper develops a non-kinematic likelihood ratio score based on statistical models for the signal-to-noise (SNR) and radar cross section (RCS) for use in narrowband radar tracking. the formulation requires an estimate of the target mean RCS, and a key challenge is the trackina of the mean RCS through significant "jumps" due to aspect dependencies. A novel multiple model approach is used track through the RCS jumps. three solution are developed: one based on an a-filter, a second based on the median filter, and the third based on an IMM filter with a median pre-filter. Simulation results are presented that show the effectiveness of the multiple model approach for tracking through RCS transitions due to aspect-angle changes.

关键词： feature-aided tracking RCS tracking multiple model signal score swerling fluctuation

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