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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2010"

共 976 条记录，以下是671-680 订阅

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Computationally efficient assignment-based algorithms for data association for tracking with angle-only sensors

Computationally efficient assignment-based algorithms for da...

引用

19th conference on signal and data processing of small targets

作者： Sathyan, T. Sinha, A. Kirubarajan, T. McMaster Univ Dept Elect & Comp Engn 1280 Main St West Hamilton ON L8S 4K1 Canada

ISBN: (纸本)9780819468475

In this paper we describe computationally efficient assignment-based algorithms to solve the data association problem in synchronous passive multisensor tracking systems. A traditional assignment-based solution to this problem is to solve the measurement-to-measurement association using multidimensional (S-dimensional or S-D with S sensors) assignment formulation and the measurement-to-track association using two-dimensional assignment formulation. Even though this solution has been proven to be effective, it is computationally very expensive. One of the reasons is that in calculating the assignment cost of each possible candidate association one requires to find the maximum likelihood (ML) estimate of the unknown target state. The algorithms proposed in this paper use prior information of the targets that are being tracked to reduce the requirement for the costly ML estimation. The first algorithm is similar to the traditional two step technique except that it uses the predicted track information to avoid building the whole assignment tree in the measurement-to-measurement association. In particular, based on the predicted track information first validation gates are constructed for every target. Then, when forming the assignment tree, only the branches connecting measurements that satisfy the validation gate requirement are constructed. The second algorithm is a one-step algorithm in that it directly assigns the measurements to the tracks. We pose the data association problem as an (S + 1)-D assignment with the first dimension being the predicted state information of the tracks, and the rest of the S dimensions are the lists of measurements from the sensors. The costs of each possible (S + 1)-tuple are calculated based on the predicted track information, hence, the requirement for an ML estimate is eliminated. Further, we show that when the target maneuvers are not very high, and when the sensor measurements are uncorrelated the (S + 1)-D assignment approximately

关键词： multitarget/multisensor tracking data association assignment technique auction algorithm passive sensor tracking

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Robust radar detection of moving ground targets with STAP

Robust radar detection of moving ground targets with STAP

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RADAR 2007 - The Institution of Engineering and Technology International conference on Radar Systems

作者： Kealey, Paul G. Carrington, David M. QinetiQ Ltd. United Kingdom

ISBN: (纸本)9780863418488

Airborne radar sensors are able to detect small and slow moving ground targets using short dwell times by distinguishing the weak target signature from the dominant ground clutter signal. The capability of a Ground Moving Target Indication (GMTI) sensor is principally governed by the Probability of Detection (PD). Other capabilities such as geolocation, tracking and recognition are derived from these detection data. One method used to enhance the PD for platforms with multiple phase centres is Space Time Adaptive processing (STAP). In this paper we report an experimental assessment of a robust STAP algorithm [1, 5, 6] using the QinetiQ Enhanced Surveillance Radar (ESR) data. From our results it is apparent that robustness may be introduced if a selective training methodology is employed to estimate the data covariance. Results are given for both Pre- and Post-Doppler STAP.

关键词： Space time adaptive processing

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Joint MAP bias estimation and data association: Algorithms

Joint MAP bias estimation and data association: Algorithms

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. A specific problem that has received some attention in the tracking literature is that of the target object map problem in which one tries match a set of tracks as observed by two different sensors in the presence of biases, which are modeled here as a translation between the track states. The general framework also applies to problems in which the costs are general nonlinear functions of the biases. The goal of this paper is to present a class of algorithms based on the branch and bound framework and the "all-pairs" and k-best heuristics that provide a good initial upper bound for a branch and bound algorithm. These heuristics can be used as part of a real-time algorithm or as part of an "anytime algorithm" within the branch and bound framework. In addition, we consider both the A*-search and depth-first search procedures as well as several efficiency improvements such as gating. While this paper focuses on the algorithms, a second paper will focus on simulations.

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

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ROBUST RADAR DETECTION OF MOVING GROUND targets WITH STAP

ROBUST RADAR DETECTION OF MOVING GROUND TARGETS WITH STAP

引用

IET International conference on Radar Systems (Radar 2007)

作者： Paul G. Kealey David M. Carrington QinetiQ Limited UK

关键词： STAP Detection Robust GMTI

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An Effective SAR-GMTI Technique Based on Eigen-Decomposition of the Covariance Matrix

An Effective SAR-GMTI Technique Based on Eigen-Decomposition...

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Asian and Pacific conference on Synthetic Aperture Radar (APSAR)

作者： Jing Yu Guisheng Liao National Lab of Radar Signal Processing Xidian University Xi'an China

An effective multi-channel SAR-GMTI technique based on eigen-decomposition of the covariance matrix is proposed. The variation of the sum of small eigenvalues of the covariance matrix is used to detect moving targets which can avoid clutter suppression. The radial velocity of the moving target is estimated by two steps. Firstly, using the interferometric phase of two SAR images to get the coarse radial velocity estimation, then the more precise radial velocity is obtained by searching the space-domain steering vector of the moving target. It overcomes the sensitivity of the interferometric phase to clutter and noise. The performance of the approach is analyzed in detail. The merit of the presented technique is demonstrated by simulated and measured SAR data.

关键词： Covariance matrix Clutter Eigenvalues and eigenfunctions Synthetic aperture radar Radar detection Phase estimation Phase noise Radar imaging Azimuth Radar signal processing

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An Effective SAR-GMTI Technique Based on Eigen-Decomposition of the Covariance Matrix

An Effective SAR-GMTI Technique Based on Eigen-Decomposition...

引用

2007 Asian and Pacific conference on Synthetic Aperture Radar(APSAR 2007)

作者： YU Jing LIAO Guisheng National Lab of Radar signal processing,Xidian University,Xi’an,710071,China

An effective multi-channel SAR-GMTI technique based on eigen-decomposition of the covariance matrix is proposed. The variation of the sum of small eigenvalues of the covariance matrix is used to detect moving targets which can avoid clutter suppression. The radial velocity of the moving target is estimated by two steps. Firstly,using the interferometric phase of two SAR images to get the coarse radial velocity estimation,then the more precise radial velocity is obtained by searching the space-domain steering vector of the moving target. It overcomes the sensitivity of the interferometric phase to clutter and noise. The performance of the approach is analyzed in detail. The merit of the presented technique is demonstrated by simulated and measured SAR data.

关键词： SAR An Effective SAR-GMTI Technique Based on Eigen-Decomposition of the Covariance Matrix

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PHD filters of second order in target number

PHD filters of second order in target number

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18th conference on signal and data processing of small targets

作者： Mahler, Ronald Lockheed Martin MS2 Tact Syst Eagan MN USA

ISBN: (纸本)0819462926

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In an earlier paper at this conference we derived a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number. Since the CPHD filter has computational complexity O(m(3)) in the number m of measurements, additional approximation is desirable. In this paper we discuss a second-order approximation called the "binomial filter."

关键词： Digital filtering Nonlinear filtering Sensors Target detection Electroactive polymers Filtering (signal processing) Motion models Picosecond phenomena Electronic filtering Linear filtering

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Peak inspecting and signal recovery methods based on triple correlation - art. no. 623608

Peak inspecting and signal recovery methods based on triple ...

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18th conference on signal and data processing of small targets

作者： Cao Hanqiang Li Xutao Chen Rujun Wei Jiaxin Cao Chao Huazhong Univ Sci & Technol Dept Elec & Info Eng Wuhan 430074 Peoples R China

ISBN: (纸本)0819462926

Weak target inspecting and recovering are very important in IR detecting systems. In this paper, triple correlation peak inspecting techniques(TCPIT) are adopted for the signal processing of IR systems in detecting sub-pixel or point targets. Investigations show that the signal-to-noise ratio (SNR) improvement of approximate 23dB can be obtained with the input peak SNR of 0.84 and the input power SNR of -0.93dB. The triple correlation overlapping sampling technique(TCOST) is advanced for restoring signal waveforms of IR detection systems. Investigations show that signal waveforms can effectively be restored in the low signal-to-noise ratio circumstances using this approach.

关键词： triple correlation weak signal inspecting detection of point target IR detecting system

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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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Joint detection and tracking of unresolved targets with a joint-bin processing monopulse radar - art. no. 62360T

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

引用

18th conference on signal and data processing of small targets

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

ISBN: (纸本)0819462926

Detection and estimation of multiple unresolved targets with a monopulse radar is limited by the availability of information in monopulse signals. The maximum possible number of targets that can be extracted from the monopulse signals of a single bin is two. Recently two approaches have been proposed in the literature to overcome this limitation. The first is joint-bin processing that exploits target spill-over among adjacent cells by modeling the target returns in the adjacent cells. In addition to making use of the additional information available in target spill-over, it handles a more practical problem where the usual assumption of ideal sampling is relaxed. The second approach is to make use of tracking information in detection through joint detection and tracking with the help of Monte Carlo integration of a particle filter. It was shown that the extraction of even more targets is possible with tracking information. In this paper, a new approach is proposed to combine make the best of these two approaches - a new joint detection and tracking algorithm with multibin processing. The proposed method increases the detection ability as well as tracking accuracy. Simulation studies are carried out. with amplitude comparison monopulse radar for an unresolved target scenario. The relative performances of various methods are also provided.

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

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