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conference on signal and data processing of small targets 2010

作者： Schoenecker, Steven Willett, Peter Bar-Shalom, Yaakov USN Naval Undersea Warfare Ctr Div Newport 1176 Howell St Newport RI 02841 USA Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819481627

In the early 1990's, the Maximum Likelihood Probabilistic data Association (ML-PDA) tracker was developed in a passive sonar framework, and subsequent research has shown it to be effective for tracking very low SNR targets. This was done with both active and passive sonar, for targets that have some given type of deterministic motion. Recent work has focused on applying ML-PDA to bistatic sonar data. Here, we apply ML-PDA in a sliding window implementation to three bistatic data sets used by the MSTWG (Multistatic Tracking Working Group): the SEABAR 2007 data set, the TNO Blind 2008 data set, and a new blind data set provided by Metron in 2009.

关键词： ML-PDA Multistatic Bistatic Active Sonar Target Tracking

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Probabilistic Tracking of Multiple Extended targets using Random Matrices

Probabilistic Tracking of Multiple Extended Targets using Ra...

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conference on signal and data processing of small targets 2010

作者： Wieneke, Monika Koch, Wolfgang Fraunhofer FKIE D-53343 Wachtberg Germany

ISBN: (纸本)9780819481627

Conventional tracking algorithms rely on the assumption that the targets under observation are point source objects. However, due to increasing resolution capabilities of modern sensors, the point source assumption is often not suitable and estimating the target extension becomes a crucial aspect. Recently, a Bayesian approach to extended target tracking using random matrices has been proposed. Within this approach, ellipsoidal object extensions are modeled by random matrices and treated as additional state variables to be estimated. However, only a single-target solution has been presented so far. In this work we present the multi-target extension of this approach. We derive a new variant of Probabilistic Multi-Hypothesis Tracking (PMHT) that simultaneously estimates the ellipsoidal shape and the kinematics of each target. For this purpose, the PMHT auxiliary function is extended by random matrices representing the target ellipsoids. Both, the ellipsoids and the kinematic states are iteratively optimized by specific Kalman filter formulae arising directly from the auxiliary function. The new method is demonstrated and evaluated by simulative examples.

关键词： PMHT Probabilistic Multi-Hypothesis Tracking Multi-Target Tracking Random Matrices Extended targets Group targets Target Clusters

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Using PMHT for separation point estimation

Using PMHT for separation point estimation

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conference on signal and data processing of small targets 2010

作者： Dunham, Darin T. August, Scott E. Vectraxx Inc Glen Allen VA 23059 USA

ISBN: (纸本)9780819481627

Target tracking is limited by the resolution of the sensors providing the measurements. If two objects are in close proximity, they will return just one measurement in most instances. When these two objects separate enough to return two distinct measurements, the question then is: where did the objects actually separate? The actual separation point may be of interest and finding that separation point is the topic of this paper. Using the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm allows measurements to be "shared" between tracks, and therefore makes an excellent algorithm when there are closely-spaced unresolved measurements. In this paper, we will give an overview of how we apply the PMHT algorithm to this separation estimation problem, and then we apply the algorithm to two aircraft flying in formation and then separating. The results are obtained from a high-fidelity simulation environment and provide a good test for this developing approach.

关键词： separating targets PMHT target tracking sensor resolution unresolved closely-spaced objects

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dataWare: Sea clutter and small boat radar reflectivity databases

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IEEE signal processing Magazine 2010年第2期27卷 145-148页

作者： De Wind, H.J. Cilliers, J.C. Herselman, P.L. CSIR Defence Peace Safety and Security Unit Pretoria South Africa Reutech Radar Systems Stellenbosch South Africa

In this issue, "Best of the Web" presents online radar reflectivity databases, which are free of charge to the international research community. databases such as these are used for the characterization of interference (including noise, sea clutter and land clutter, among others) in radar systems, and the development of signal processing techniques that reduce the detrimental effects of this unwanted interference. The data can also be used for the development and evaluation of signal processing techniques aimed at detecting and tracking man-made objects by separating targets from interference based on Doppler and amplitude characteristics. These signal processing techniques include moving target indication (MTI), pulse-Doppler, space-time adaptive processing, track-before-detect (TkBD), and constant false alarm rate (CFAR) processing. © 2006 IEEE.

关键词： database systems

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Probabilistic data Association in High Clutter Environments

Probabilistic Data Association in High Clutter Environments

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conference on signal and data processing of small targets 2010

作者： Tharmarasa, R. Lang, T. McDonald, Mike Kirubarajan, T. McMaster Univ Hamilton ON Canada Gen Dynam Canada Ottawa ON Canada Def Res & Dev Canada Ottawa ON Canada

ISBN: (纸本)9780819481627

data association is the key component in single or multiple target tracking algorithms with measurement origin. Probabilistic data Association (PDA), in which all validated measurements are associated probabilistically to the predicted estimate, is a well-known method to handle the measurement origin uncertainty. In PDA, the effect of measurement origin uncertainty is incorporated into the updated covariance by adding the spread of the innovations term. The updated covariance may become very large after few time steps in high clutter scenarios due to spread of the innovations term. Large covariance results in a large gate, which is used to limit the possible measurements that could have originated from the target. Hence, the track will be lost and estimate will just follow the prediction. Also, large gate will make the well-separated target assumption invalid, even if the targets are well-separated. Hence, after a few time steps all the targets in the surveillance region come under the same group, making the Joint Probabilistic data Association (JPDA). In this paper, adaptive gating techniques are proposed to avoid the steady increase in the updated covariance in high clutter. The effectiveness of the proposed techniques is demonstrated on simulated data.

关键词： Probabilistic data association high clutter target tracking measurement origin uncertainty adaptive gating

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The JPDAF in Practical Systems: Computation and Snake Oil

The JPDAF in Practical Systems: Computation and Snake Oil

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conference on signal and data processing of small targets 2010

作者： Crouse, David F. Bar-Shalom, Yaakov Willett, Peter Svensson, Lennart Univ Connecticut Dept Elect & Comp Engn 371 Fairfield WayU-2157 Storrs CT 06269 USA Chalmers Univ Technol Dept Signal & Syst Gothenburg Sweden

ISBN: (纸本)9780819481627

In this paper we look at various options for calculating target-measurement association probabilities and updating the state estimates in the Joint Probabilistic data Association Filter (JPDAF). In addition to the "standard" methods, we look at other methods that try to improve the estimation accuracy by coupling the states, discarding certain joint association events, or by applying random finite set theory to change how the states are updated. We compare the performance of trackers based on several of these concepts to each other and to the PMHT, the MHT, and the GNN tracker. We also single out approaches that are "snake oil", in that they are either not suited for practical use, or that their complexity is higher than that of calculating the probabilities exactly. Additionally we show how the JPDAF* can be implemented to have a lower worst-case complexity than the regular JPDAF when the number of targets and/or observations is large. We also review some oft overlooked references on gating that are useful for implementations in real systems.

关键词： Detection and tracking algorithms Target detection Monte Carlo methods Motion models Computing systems Filtering (signal processing) Motion measurement Nomenclature Computer simulations Radar

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Track Segment Association for Ground Moving targets with Evasive Move-Stop-Move Maneuvers

Track Segment Association for Ground Moving Targets with Eva...

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conference on signal and data processing of small targets 2010

作者： Zhang, Shuo Bar-Shalom, Yaakov Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819481627

In a real tracking system, track breakages can occur due to highly maneuvering targets, low detection probability, or clutter. Previously, a track segment association approach (TSA) was developed for an airborne early warning (AEW) system to improve track continuity by "stitching" broken track segments pertaining to the same target. However, this technique cannot provide satisfactory association performance in tracking with a GMTI radar ground moving targets employing evasive move-stop-move maneuvers. To avoid detection by a GMTI radar, targets can deliberately stop for some time before moving again. Since a GMTI radar does not detect a target when the radial velocity (along the line-of-sight from the sensor) falls below a certain minimum detectable velocity (MDV), the move-stop-move maneuvers of the targets usually lead to broken tracks as a result. We present a new TSA technique which employs a dummy track to formulate a complete association. By using an IMM estimator with state-dependent mode transition probabilities (IMM-SDP) for track segment prediction (forward and backward), the proposed algorithm can effectively stitch both "regular" broken tracks and broken tracks due to targets' move-stop-move maneuvers. Comparisons are given to show the advantages of the proposed algorithm in broken tracks reduction and track continuity improvement.

关键词： Target detection 3D acquisition Radar data transmission Detection and tracking algorithms Image segmentation Sensors Performance modeling Process modeling Error analysis

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Generalized particle flow for nonlinear filters

Generalized particle flow for nonlinear filters

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conference on signal and data processing of small targets 2010

作者： Daum, Fred Huang, Jim Raytheon 225 Presidential Way Woburn MA 01801 USA

ISBN: (纸本)9780819481627

We generalize the theory of particle flow to stabilize the nonlinear filter. We have invented a new nonlinear filter that is vastly superior to the classic particle filter and the extended Kalman filter (EKF). In particular, the computational complexity of the new filter is many orders of magnitude less than the classic particle filter with optimal estimation accuracy for problems with dimension greater than 4. Our accuracy is typically several orders of magnitude better than the EKF for nonlinear problems. We do not resample, and we do not use any proposal density from an EKF or UKF or other filter. Moreover, our new algorithm is deterministic, and we do not use any MCMC methods;this is a radical departure from other particle filters. The new filter implements Bayes' rule using particle flow rather than with a pointwise multiplication of two functions;this avoids one of the fundamental and well known problems in particle filters, namely "particle degeneracy." In addition, we explicitly stabilize our particle filter using negative feedback, unlike standard particle filters, which are generally very inaccurate for plants with slow mixing or unstable dynamics. This stabilization improves performance by several orders of magnitude for difficult problems.

关键词： Particles Particle filters Nonlinear filtering Filtering (signal processing) Negative feedback Linear filtering Control systems signal to noise ratio Electronic filtering signal processing

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Identifying chemicals from their Raman spectra using minimum description length

Identifying chemicals from their Raman spectra using minimum...

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conference on signal and data processing of small targets 2010

作者： Palkki, Ryan D. Lanterman, Aaron D. Georgia Inst Technol Ctr Signal & Image Proc Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9780819481627

Raman spectroscopy has been a powerful means of chemical identification in a variety of fields, partly because of its non-contact nature and the speed at which measurements can be taken. Given a library of known Raman spectra, a common detection approach is to first estimate the relative amount of each chemical present, and then compare the estimated mixing coefficients to an ad hoc threshold. We present a more rigorous detection scheme by formulating the problem as one of Multiple Hypothesis Detection (MHD) and using the maximum a posteriori (MAP) decision rule to minimize the probability of classification error. The probability that a specific target chemical is present is estimated by summing the estimated probabilities of all the hypotheses containing it. Alternatively, since we do not typically have reasonable priors for the hypotheses, it is perhaps preferable to interpret the result as an abstract score corresponding to the Minimum Description Length (MDL) approach. The resulting detection performance of this approach is compared to that of several other classification algorithms.

关键词： Raman spectroscopy detection classification minimum description length (MDL)

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IMPROVED HYPERSPECTRAL LAND-COVER ANALYSIS USING RELEVANCE VECTOR MACHINE

IMPROVED HYPERSPECTRAL LAND-COVER ANALYSIS USING RELEVANCE V...

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IEEE International conference on Image processing

作者： Mianji, Fereidoun A. Zhang, Ye Harbin Inst Technol Sch Elect & Informat Tech Harbin Peoples R China

ISBN: (纸本)9781424479948

In land-cover analysis of hyperspectral remotely sensed images through supervised classification methods, a frequent problem is that the available training samples corresponding to different land-covers are not sufficient. This problem is especially severe for small land-covers and targets which often include the key information of the scene. Furthermore, degrading due to "boundary effect" is more serious in classification of small and scattered patches of land-covers. In this paper, a new supervised hyperspectral classification method through application of a discriminant data transformation in joint with a Bayesian learning-based probabilistic sparse kernel model, i.e., relevance vector machine (RVM), is proposed. The proposed method outperforms other efficient approaches in terms of classification accuracy, robustness to Hughes phenomenon (lack of accuracy due to too small ratio of training sample number to feature number), and computational complexity in particular for small and scattered land-cover classes which are harder to be precisely classified.

关键词： Hughes phenomenon hyperspectral data relevance vector machine remote sensing supervised classification

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