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Sensors 2016, 16(6), 901;

Multi-Target State Extraction for the SMC-PHD Filter

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Author to whom correspondence should be addressed.
Academic Editor: Xue Wang
Received: 27 March 2016 / Revised: 3 June 2016 / Accepted: 8 June 2016 / Published: 17 June 2016
(This article belongs to the Section Physical Sensors)
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The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing potential target locations. To address this problem, a novel multi-target state extraction algorithm is proposed in this paper. By exploiting the information of measurements and particle likelihoods in the filtering stage, we propose a validation mechanism which aims at selecting effective measurements and particles corresponding to detected targets. Subsequently, the state estimates of the detected and undetected targets are performed separately: the former are obtained from the particle clusters directed by effective measurements, while the latter are obtained from the particles corresponding to undetected targets via clustering method. Simulation results demonstrate that the proposed method yields better estimation accuracy and reliability compared to existing methods. View Full-Text
Keywords: multi-target tracking; probability hypothesis density filter; state extraction multi-target tracking; probability hypothesis density filter; state extraction

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Si, W.; Wang, L.; Qu, Z. Multi-Target State Extraction for the SMC-PHD Filter. Sensors 2016, 16, 901.

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