Tracking and classifying multiple targets without&amp;lt;tex&amp;gt;a priori&amp;lt;/tex&amp;gt;identification

Mori, Shintaro; Chong, Chee-Yee; Tse, Edison; Wishner, R. P.

doi:10.1109/tac.1986.1104306

Cited by 140 publications

(120 citation statements)

References 11 publications

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“…For instance, the posterior probability that targets are present in region of the state space is . Assuming is available the JMPD at time is updated via the recursion [13], [31] (8)…”

Section: Notation and Modelingmentioning

confidence: 99%

A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

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Abstract-This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.

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“…For instance, the posterior probability that targets are present in region of the state space is . Assuming is available the JMPD at time is updated via the recursion [13], [31] (8)…”

Section: Notation and Modelingmentioning

confidence: 99%

A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

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“…Several methods for computing the infor-target track. A more precise formulation of the problem can mation gains are discussed in [17], and experimented in be found in [19]. simulations and on field data from sensor network test beds.…”

Section: P(zj (T) I X (T)) For the Measurement Z (T) If Target Utilmentioning

confidence: 99%

“…Moreover, the 4.1 Model collection of hypotheses formed and evaluated on any given information set can be decomposed into stochastically We assume that there are multiple targets i =1,.., N. independent components, each called a stochastic cluster or Each target i has a state xi (t) at time t. The state transition simply a cluster. The measurement-oriented MHT algorithm for each target is independent of that of another target and first developed in [23] and generalized in [19] consists of given by the conditional probability P(x, (tkl ) I x, (tk)).…”

Section: Basic Algorithm T Sensoramentioning

confidence: 99%

Distributed tracking in wireless ad hoc sensor networks

Chong

Zhao²,

Mori³

et al. 2003

Sixth International Conference of Information Fusion, 2003. Proceedings of The

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“…The earliest published work using a point process formalism for multi-target filtering appears to be [35]. A point process based filter was also proposed in [47] to estimate an unknown but fixed number of targets.…”

Section: Introductionmentioning

confidence: 99%

Sequential monte carlo methods for multi-target filtering with random finite sets

Singh

Doucet

2005

IEEE Trans. Aerosp. Electron. Syst.

957

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Abstract-Random finite sets are natural representations of multi-target states and observations that allow multi-sensor multi-target filtering to fit in the unifying random set framework for Data Fusion. Although the foundation has been established in the form of Finite Set Statistics (FISST), its relationship to conventional probability is not clear. Furthermore, optimal Bayesian multi-target filtering is not yet practical due to the inherent computational hurdle. Even the Probability Hypothesis Density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multi-target posterior, still involves multiple integrals with no closed forms in general. This article establishes the relationship between FISST and conventional probability that leads to the development of a sequential Monte Carlo (SMC) multi-target filter. In addition, a SMC implementation of the PHD filter is proposed and demonstrated on a number of simulated scenarios. Both of the proposed filters are suitable for problems involving non-linear non-Gaussian dynamics. Convergence results for these filters are also established.

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Tracking and classifying multiple targets without<tex>a priori</tex>identification

Cited by 140 publications

References 11 publications

A Bayesian Approach to Multiple Target Detection and Tracking

A Bayesian Approach to Multiple Target Detection and Tracking

Distributed tracking in wireless ad hoc sensor networks

Sequential monte carlo methods for multi-target filtering with random finite sets

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