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检索条件"任意字段=13th Conference on Signal and Data Processing of Small Targets"

共 448 条记录，以下是281-290 订阅

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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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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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Bias estimation using targets of opportunity

Bias estimation using targets of opportunity

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19th conference on signal and data processing of small targets

作者： Kragel, Bret D. Danford, Scott Herman, Shawn M. Poore, Aubrey B. Numer Corp 4850 Hahns Peak Dr Loveland CO 80538 USA

ISBN: (纸本)9780819468475

Fusion of data from multiple sensors can be hindered by systematic errors known as biases. Specifically, the presence of biases can lead to data misassociation and redundant tracks. Fortunately, if an estimate of the unknown biases can be obtained, the measurements and transformations for each sensor can be debiased prior to fusion. In this paper, we present an algorithm that uses targets of opportunity in the sensor field-of-view for online estimation of time-variant biases. the algorithm uses the singular value decomposition (SVD) to automatically handle the issue of parameter observability during tracking, allowing for shorter estimation windows and more accurate bias estimation. Our approach extends the novel methods proposed in the companion paper(1) by Herman and Poore that used the SVD within a nonlinear least-squares estimator to handle the issue of parameter observability during offline estimation of time-invariant biases using truth data.

关键词： bias estimation sensor registration gridlock nonlinear least-squares singular value decomposition bias observability

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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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Map integration in tracking

Map integration in tracking

引用

19th conference on signal and data processing of small targets

作者： Sworder, David D. Boyd, John E. Hutchins, R. G. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Cub Def Applicat San Diego CA 92186 USA Naval Postgrad Sch Dept EECE Monterey CA USA

ISBN: (纸本)9780819468475

A multiple model tracking algorithm has considerable advantage when the target is moving on a road grid. A map-enhanced algorithm magnifies this by excluding large regions of the 2D motion-space from search. However, integrating the grid of streets and junctions into a recursive algorithm is challenging. this paper presents a map-enhanced, tracker that uses local models tuned to motions on the four cardinal directions. Map compliance is achieved by re-initializing the local kinematic state when a modal change occurs and by moving the local estimate to the map after a measurement update. Unfortunately, the transition dynamics of the modal state depend upon the kinematic state. A proposed junction influence function is based upon a rapidly decaying measure of the distance from the target to the nearest compatible junction.

关键词： nonlinear filters target tracking data fusion

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Joint MAP bias estimation and data association: Simulations

Joint MAP bias estimation and data association: Simulations

引用

19th conference on signal and data processing of small targets

作者： Danford, Scott Kragel, Bret Poore, Aubrey Numer Corp POB 271246 Ft Collins CO 80527 USA

ISBN: (纸本)9780819468475

the problem of joint maximum a posteriori (MAP) bias estimation and data association belongs to a class of nonconvex mixed integer nonlinear programming problems. these problems are difficult to solve due to both the combinatorial nature of the problem and the nonconvexity of the objective function or constraints. Algorithms for this class of problems have been developed in a companion paper of the authors. this paper presents simulations that compare the "all-pairs" heuristic, the k-best heuristic, and a partial A*-based branch and bound algorithm. the combination of the latter two algorithms is an excellent candidate for use in a realtime system. For an optimal algorithm that also computes the k-best solutions of the joint MAP bias estimation problem and data association problem, we investigate a branch and bound framework that employs either a depth-first algorithm or an A*-search procedure. In addition, we demonstrate the improvements due to a new gating procedure.

关键词： MAP bias estimation data association heuristics A*-search branch and bound K-best solutions nonconvex MINLP

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Comparison of bias removal algorithms in track-to-track association

Comparison of bias removal algorithms in track-to-track asso...

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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 compares the performance of several algorithms for estimating relative sensor biases when two sets of sensor tracks from two sensor systems are to be fused to form system tracks. the primary focus of this paper is the algorithms' performance, particularly in terms of the mean-square estimation error criterion. the efficiency of the algorithms is not our focus for this study. We are especially interested in three estimation algorithms: (1) the joint track-association/relative-bias-estimation maximum a posteriori (MAP) probability-density/probability-mass function algorithm;(2) the marginal MAP probability density estimation algorithm;and (3) the minimum-variance (MV) estimation algorithm. those algorithms rely on the capability of generating and evaluating multiple significant track-to-track association hypotheses, which may be obtained by any of the recently developed k-best bipartite data assignment algorithms. Several other algorithms that have been considered in the past will also be discussed.

关键词： multiple target tracking hierarchical data fusion track-to-track association relative bias estimation and removal

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Electromagnetic target recognition with the fused MUSIC spectrum matrix method:: Applications and performance analysis for incomplete frequency data

Electromagnetic target recognition with the fused MUSIC spec...

引用

IEEE 15th signal processing and Communications Applications conference

作者： Secmen, Mustafa Ekmekci, Evren Turhan-Sayan, Goenuel Orta Dogu Tek Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781424407194

the aim of this paper is to apply an electromagnetic target recognition method, which is based on the use of fused MUSIC spectrum matrices, to the case of incomplete frequency domain data. the aforementioned method was suggested recently and succesfully applied to both canonical and complicated targets in the presence of complete frequency domain data [1]. However, most of the real world applications involve the use of severely incomplete frequency data, especially missing low frequency information. In this paper, the performance of mentioned target recognition method is investigated for various incomplete frequency bands and for a group of five small-scale aircraft targets. As a result, the mentioned target recognition method is demonstrated to give still high correct decision rates even when the frequency band is reduced by 300 MHz at low frequency end and at the signal-to-noise ratio is decreased down to -5 dB level.

关键词： Frequency domain analysis

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Future prospects for algorithm development of tracking related processing - art. no. 66990E

Future prospects for algorithm development of tracking relat...

引用

19th conference on signal and data processing of small targets

作者： Drummond, Oliver E. CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)9780819468475

the state of the art of tracking has matured and consequently, the priorities for improved performance and expanded or new processing capabilities have changed. Future directions in algorithm development in tracking and related data processing are not easy to predict with accuracy. the future priorities of development tasks predicted in this presentation are subjective;that is, simply the author's view. While there will continue to be algorithm development to improve many aspects of tracking, the emphasis is expected to change in favor of expanded and new capabilities. this presentation will address not only those tracking algorithms that will have higher priority for continued algorithm development but also those aspects of tracking related processing that are expected to be of special interest. In addition, to facilitate this discussion, the categories of the state of the art of tracking are more finely decomposed. Many aspects of single sensor, multiple target tracking have matured during the last 20 years but room for improvement remains. In contrast, fusion of data from multiple distributed sensors is not as mature and interest is expected to continue to increase. Many fusion systems pose challenges that are not of much concern in tracking with data from a single sensor and algorithm development of those aspects of fusion will continue to be needed. the capability of the many functions and users of the output of trackers needs to be improved and expanded. Consequently, an increase is expected in the need to improve the content and quality of the output of both the single sensor and fusing trackers. the identified aspects of the new needs in tracker output are discussed and a dialog on this topic is encouraged.

关键词： multiple target tracking tracking with data from multiple sensors sensor data fusion network centric processing tracking open issues Bayesian methods Kalman filter

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Tracking dim targets using integrated clutter estimation

Tracking dim targets using integrated clutter estimation

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19th conference on signal and data processing of small targets

作者： Brekke, Edmund Kirubarajan, thiagalingam tharmarasa, Ratnasingharn Norwegian Univ Sci & Technol Dept Engn Cybernet NO-7491 Trondheim Norway Univ Grad Ctr NO-2027 Kjeller Norway McMaster Univ Elect & Comp Engn Hamilton ON L8S 4K1 Canada

ISBN: (纸本)9780819468475

In this paper we address the problem of detecting and tracking a single dim target in unknown background noise. Several methodologies have been developed for this problem, including track-before-detect (TBD) methods which work directly on unthresholded sensor data. the utilization of unthresholded data is essential when signal-to-noise ratio (SNR) is low, since the target amplitude may never be strong enough to exceed any reasonable threshold. Several problems arise when working with unthresholded data. Blurring and non-Gaussian noise can easily lead to very complicated likelihood expressions. the back-round noise also needs to be estimated. this estimate is a random variable due to the random nature of the background noise. We propose a recursive TBD method which estimates the background noise as part of its likelihood evaluation. the background noise is estimated by averaging over nearby sensor cells not affected by the target. the uncertainty of this estimate is taken into account by the likelihood evaluation, thereby yielding a more robust TBD method. the method is implemented using sequential Monte Carlo evaluation of the optimal Bayes equations, also known as particle filtering. Simulation results show how our method allows detection and tracking to be carried out in an uncertain environment where current recursive TBD methods fail.

关键词： track-before-detect dim target clutter estimation particle filter

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