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Application of the Kalman-Levy filter for tracking maneuvering targets

Application of the Kalman-Levy filter for tracking maneuveri...

引用

16th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T Bar-Shalom, Y McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)081945351X

In target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an Interacting Multiple Model - IMM - estimator). the over-simplification resulting from the above assumptions can cause severe degradation in tracking performance. Of particular concern is the simplistic white noise or Wiener process acceleration models used to handle maneuvering targets. Presence of heavy-tailed noise in the observation process is another concern. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. this filter assumes a heavy tailed noise distribution known as the Levy distribution. Unlike in the case of Gaussian distribution, the existence of the covariance is not guaranteed in this case. Due to the heavy tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of Kalman-Levy filter in non-maneuvering portion of track is worse than a Kalman filter's. this motivates us to develop an IMM estimator incorporating a Kalman filter and a Kalman-Levy filter. the performance of this filter is compared with an IMM estimator with two standard Kalman filters in a scenario from the 4th Navy tracking benchmark problem. It is found that the IMM estimator with a Kalman-Levy filter performs better than the other IMM estimator in both maneuvering and non-maneuvering portion of target flight.

关键词： maneuvering target tracking interacting multiple model filter Kalman filter Kalman-Levy filter heavy-tailed process noise

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Feature aided tracking (FAT)

Feature aided tracking (FAT)

引用

16th conference on signal and data processing of small targets

作者： Singer, PF Coursey, AL Raytheon Co El Segundo CA 90245 USA

ISBN: (纸本)081945351X

Association of observations and tracks is a fundamental component of most solutions to the tracking problem. Association is frequently formulated as a multiple hypothesis test. Typically, the test statistic, called the track score, is the likelihood or likelihood ratio of the observations conditioned upon the association hypotheses. Assuming that the test is reasonably efficient, further reduction in the association error probability necessitates the introduction of additional information into the track score. this additional information is embodied in quantities called track features which are to be included in the track score. In practice, the necessary conditional probabilities of the track features are unknown. the class of non-parametric hypothesis tests is designed to provide such a test in the absence of any probabilistic information about the data. However, the test statistics used in non-parametric tests cannot be used directly in the track score. the one probabilistic quantity generally available with non-parametric tests is the Type I error probability, the probability of failing to accept a true hypothesis. If the non-parametric test is distribution free then the Type I error probability is independent of the distribution of the track features. this paper presents a distribution free, non-parametric test of the track features that can be used to test the association hypotheses and a quantity that can be included in the track score is derived from the Type I error probability of the test.

关键词： feature aided tracking non-parametric hypothesis test distribution free data association

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A multiple model probability hypothesis density filter for tracking maneuvering targets

A multiple model probability hypothesis density filter for t...

引用

16th conference on signal and data processing of small targets

作者： Punithakumar, K Kirubarajan, T Sinha, A McMaster Univ ECE Dept Hamilton ON L8S 4K1 Canada

ISBN: (纸本)081945351X

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. the problem becomes even more complicated when the target number varies, in which case the dimensionality of the state space itself becomes a discrete random variable. the Probability Hypothesis Density (PHD) filter, which propagates only the first-order statistical moment (or the PHD) of the full target posterior. has been shown to be a computationally efficient solution to multitarget tracking problems with varying number of targets. the integral of PHD in any region of the state space gives the expected number of targets in that re-ion. With maneuvering targets, detecting and tracking the changes in the target motion model also become important;but current PHD implementations do not provide a mechanism for handling this. the target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-conditioned estimates in a manner similar to the one used in the Interacting Multiple Model (IMM) estimator. In this paper a multiple model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using Sequential Monte Carlo methods, is proposed. the resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over sin-le-model PHD filters.

关键词： maneuvering target tracking nonlinear filtering probability hypothesis density filter sequential Monte Carlo methods multiple model estimation

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Chirp pulse microwave computed tomography(CP-MCT) equipped with a fan beam scanner for high-speed imaging of a biological target

Chirp pulse microwave computed tomography(CP-MCT) equipped w...

引用

25th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2003

作者： Miyakawa, M. Harada, E. Jing, W. Center for Transdisciplinary Research Niigata University Niigata Japan Graduate School of Natural Science and Technology Niigata University Niigata Japan

ISBN: (纸本)0780377893

Recently, microwave imaging aiming at early-stage-tumor detection has become of major interest because of its relatively high contrast resolution in the image. Chirp pulse microwave computed tomography (CP-MCT) obtains the attenuation and/or phase shift images of biological targets by use of a chirp pulse signal from 1 GHz to 2 GHz and signal processing techniques. the experimental- and computational-studies show that the estimated spatial resolution becomes 10 to 12 mm and very small temperature variation such as 0.3 degree to 0.5 degree is detectable. Usefulness of this imaging modality increases by developing a fan beam scanner that enables fast data acquisition. In fact, it takes only a few minutes for imaging, while the prototype system takes about 90 minutes. © 2003 IEEE.

关键词： data acquisition

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Chirp pulse microwave computed tomography(CP-MCT) equipped with a fan beam scanner for high-speed imaging of a biological target

Chirp pulse microwave computed tomography(CP-MCT) equipped w...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： M. Miyakawa E. Harada W. Jing Center for Transdisciplinary Research Niigata University Niigata Japan Graduate School ofNatural Science and Technology NiiRata University Niigata Japan

关键词： Chirp Biology computing Microwave imaging High-resolution imaging Spatial resolution Image resolution signal resolution Microwave theory and techniques Computed tomography Attenuation

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Detection of small IR objects using wavelets, matched subspace detectors, and registration

Detection of small IR objects using wavelets, matched subspa...

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14th conference on signal and data processing of small targets

作者： Hewer, G Kuo, W Kenney, C Hanson, G Bobinchak, J USN Air Warfare Ctr China Lake CA 93555 USA

ISBN: (纸本)0819444782

Infrared sensors and advanced signal processing are used to detect small (or "point") targets in highly cluttered and noisy environments. In this paper, a wavelet detection algorithm and tracking of small targets in clutter will be discussed. A new registration algorithm based on optical flow estimates with matched subspace detectors against small maneuverable targets is also discussed. Both detectors incorporate adaptive constant false alarm rate (CFAR) detection statistics. Simulation of the detection and tracking algorithms using an unclassified database with a helicopter target and platform for the video cameras is summarized.

关键词： wavelets matched subspace detector registration

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Radar detection of multiple moving targets in clutter using time-frequency-radon transform

Radar detection of multiple moving targets in clutter using ...

引用

14th conference on signal and data processing of small targets

作者： Chen, VC USN Res Lab Div Radar Washington DC 20375 USA

ISBN: (纸本)0819444782

In this paper, we analyze features of radar returns from moving targets, introduce the basic concept of time-frequency-Radon transforms, describe the Radon transform for line feature detection, discuss their applications to detection of multiple moving targets in clutter, and demonstrate two examples of moving target detection using simulated radar data.

关键词： time-frequency transform Radon transform moving targets multiple targets

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Bayesian approach to avoiding track seduction

Bayesian approach to avoiding track seduction

引用

14th conference on signal and data processing of small targets

作者： Salmond, DJ Everett, NO QinetiQ Farnborough GU14 0LX Hants England

ISBN: (纸本)0819444782

the problem of maintaining track on a primary target in the presence spurious objects is addressed. Recursive and batch filtering approaches are developed. For the recursive approach, a Bayesian track splitting filter is derived which spawns candidate tracks if there is a possibility of measurement misassociation. the filter evaluates the probability of each candidate track being associated with the primary target. the batch filter is a Markov-chain Monte Carlo (MCMC) algorithm which fits the observed data sequence to models of target dynamics and measurement-track association. Simulation results are presented.

关键词： Bayesian methods target tracking multiple targets JPDAF MCMC

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Alternative architectures for IMM tracking of maneuvering aircraft

Alternative architectures for IMM tracking of maneuvering ai...

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14th conference on signal and data processing of small targets

作者： Hutchins, RG Wilson, D Allred, LK Duren, R USN Postgrad Sch Monterey CA USA

ISBN: (纸本)0819444782

this paper presents the results of a study of tracking algorithms for maneuvering targets. the design focuses on alternative algorithms to track two-dimensional targets during maneuvers. the algorithms explored include a standard Kalman algorithm, an extended Kalman algorithm in which the target turn rate is an additional state variable, an interactive multiple model (IMM) algorithm consisting of two models with varying plant noise, a three-model IMM specifying three distinct target turn rates, and a constant gain alpha-beta filter. the IMM trackers tended to work the best in this study, with the three-model IMM performing the best overall.

关键词： target tracking maneuvering targets interactive multiple models

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Real-world tracking system performance evaluation

Real-world tracking system performance evaluation

引用

14th conference on signal and data processing of small targets

作者： Blackman, SS Dempster, RJ Raytheon Elect Syst El Segundo CA 90245 USA

ISBN: (纸本)0819444782

Preliminary tracking system design and analysis is typically done using simulated data in which the target truth is known and many techniques have been developed for evaluating performance under these conditions. However, there is a notable lack of any consistent approach for evaluating tracker performance for "real" data in which there may be an unknown number of "targets of opportunity" whose trajectories are not known. In addition, the background clutter/false alarm environment may be unknown so that an important analysis task is to determine the most accurate background models. this paper proposes a set of criteria for evaluating the tracks that are formed using "real" data collected in the field in the presence of an unknown number of "targets of opportunity". these criteria include duration, update history, and measures of kinematic and data association consistency. A scoring method is developed and the use of these criteria for system design is discussed.

关键词： multiple target tracking performance evaluation real data

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