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Sensors 2017, 17(12), 2865; https://doi.org/10.3390/s17122865

Markov Chain Realization of Joint Integrated Probabilistic Data Association

1
5th Development Division, Agency for Defense Development, P.O.Box 35, Daejeon, Korea
2
Department of Electronic Systems Engineering, Hanyang University, Ansan, 15588, Korea
*
Author to whom correspondence should be addressed.
Received: 27 October 2017 / Revised: 21 November 2017 / Accepted: 7 December 2017 / Published: 10 December 2017
(This article belongs to the Section Physical Sensors)
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Abstract

A practical probabilistic data association filter is proposed for tracking multiple targets in clutter. The number of joint data association events increases combinatorially with the number of measurements and the number of targets, which may become computationally impractical for even small numbers of closely located targets in real target-tracking applications in heavily cluttered environments. In this paper, a Markov chain model is proposed to generate a set of feasible joint events (FJEs) for multiple target tracking that is used to approximate the multi-target data association probabilities and the probabilities of target existence of joint integrated probabilistic data association (JIPDA). A Markov chain with the transition probabilities obtained from the integrated probabilistic data association (IPDA) for single-target tracking is designed to generate a random sequence composed of the predetermined number of FJEs without incurring additional computational cost. The FJEs generated are adjusted for the multi-target tracking environment. A computationally tractable set of these random sequences is utilized to evaluate the track-to-measurement association probabilities such that the computational burden is substantially reduced compared to the JIPDA algorithm. By a series of simulations, the track confirmation rates and target retention statistics of the proposed algorithm are compared with the other existing algorithms including JIPDA to show the effectiveness of the proposed algorithm. View Full-Text
Keywords: Markov chain data association; JIPDA; target existence; multi-target tracking Markov chain data association; JIPDA; target existence; multi-target tracking
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Lee, E.H.; Zhang, Q.; Song, T.L. Markov Chain Realization of Joint Integrated Probabilistic Data Association. Sensors 2017, 17, 2865.

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