Multisensor Track Management for Targets with Fluctuating SNR

Blanding, Wayne; Willett, Peter; Bar‐Shalom, Yaakov; Coraluppi, Stefano

doi:10.1109/taes.2009.5310298

Cited by 24 publications

(19 citation statements)

References 12 publications

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“…Below we consider several examples that are of interest in certain applications (see, e.g., [3,21,22]). While our main concern in this paper is checking conditions under which the Shiryaev detection procedure is asymptotically optimal, another important issue is the feasibility of its implementation, i.e., computing the Shiryaev decision statistic.…”

Section: Examplesmentioning

confidence: 99%

“…In particular, if f (y) and g l (y) are Gaussian densities, then the Shiryaev procedure minimizes all positive moments of the delay to detection. In [3], this problem was considered in the context of target track management, specifically for termination of tracks from targets with drastically fluctuating signal-to-noise ratios in active sonar systems. This drastic fluctuation was proposed to model as Markovian switches between low and high intensity signals, which lead to low and high probabilities of detection.…”

Section: Example 1: Target Track Management Applicationmentioning

confidence: 99%

“…Moreover, even these particular examples show that the verification of this condition typically represents a hard task. At the same time, there is a class of very important stochastic models -hidden Markov models (HMM) -that find extraordinary applications in a wide variety of fields such as speech recognition [13,20]; handwritten recognition [12,14]; computational molecular biology and bioinformatics, including DNA and protein modeling [4]; human activity recognition [33]; target detection and tracking [3,30,31]; and modeling, rapid detection and tracking of malicious activity of terrorist groups [21,22], to name a few. Our first goal is to focus on this class of models and specify the general results of Tartakovsky and Veeravalli [29] for HMMs, finding a set of general conditions under which the Shiryaev change-point detection procedure is asymptotically optimal as the probability of false alarm goes to zero.…”

Section: Introductionmentioning

confidence: 99%

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Asymptotic Bayesian Theory of Quickest Change Detection for Hidden Markov Models

Fuh

Tartakovsky

2019

IEEE Trans. Inform. Theory

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In the 1960s, Shiryaev developed a Bayesian theory of change-point detection in the i.i.d. case, which was generalized in the beginning of the 2000s by Tartakovsky and Veeravalli for general stochastic models assuming a certain stability of the log-likelihood ratio process. Hidden Markov models represent a wide class of stochastic processes that are very useful in a variety of applications. In this paper, we investigate the performance of the Bayesian Shiryaev change-point detection rule for hidden Markov models. We propose a set of regularity conditions under which the Shiryaev procedure is first-order asymptotically optimal in a Bayesian context, minimizing moments of the detection delay up to certain order asymptotically as the probability of false alarm goes to zero. The developed theory for hidden Markov models is based on Markov chain representation for the likelihood ratio and r-quick convergence for Markov random walks. In addition, applying Markov nonlinear renewal theory, we present a high-order asymptotic approximation for the expected delay to detection of the Shiryaev detection rule. Asymptotic properties of another popular change detection rule, the Shiryaev-Roberts rule, is studied as well. Some interesting examples are given for illustration.

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Section: Examplesmentioning

confidence: 99%

Section: Example 1: Target Track Management Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Asymptotic Bayesian Theory of Quickest Change Detection for Hidden Markov Models

Fuh

Tartakovsky

2019

IEEE Trans. Inform. Theory

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“…k is the number of clutter at time k. In the vein of [43], we use a two-value variable P i d (e i k ) to represent the time-varying and target visibility state-dependent detection probability. Specifically, P i d (e i k = 1) = p i d and P i d (e i k = 0) = ε, where ε is a small positive 4 real number (e.g., ε = 0.1).…”

Section: Data Association and Mode-model Associationmentioning

confidence: 99%

A message passing approach for multiple maneuvering target tracking

Lan

Wang

et al. 2020

Signal Processing

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This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli.

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“…This solves the early deletion problem. Meanwhile, similar to the use of an SSPRT to implement maneuver termination in [17] and track termination in [18], an extra SSPRT is used to delete tracks in this paper. To keep track deletion and confirmation being consistent, the deletion SSPRT in this paper is set to have the same logic as the confirmation SSPRT at the confirmation stage.…”

Section: Introductionmentioning

confidence: 99%

Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test

Jinbin

Sun

Zhang

2016

Sci. China Inf. Sci.

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To date, Wald sequential probability ratio test (WSPRT) has been widely applied to track management of multiple hypothesis tracking (MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT (SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.

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Multisensor Track Management for Targets with Fluctuating SNR

Cited by 24 publications

References 12 publications

Asymptotic Bayesian Theory of Quickest Change Detection for Hidden Markov Models

Asymptotic Bayesian Theory of Quickest Change Detection for Hidden Markov Models

A message passing approach for multiple maneuvering target tracking

Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test

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