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Open AccessArticle

Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking

1
Laboratory of Array and Information Processing, Hohai University, Nanjing 210098, China
2
College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4416; https://doi.org/10.3390/s18124416
Received: 15 November 2018 / Revised: 6 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
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Abstract

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness. View Full-Text
Keywords: random finite sets; Bayesian filtering; sampling time diversity; radar multi-target tracking; generalized labeled multi-Bernoulli; probability hypothesis density random finite sets; Bayesian filtering; sampling time diversity; radar multi-target tracking; generalized labeled multi-Bernoulli; probability hypothesis density
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Jiang, D.; Liu, M.; Gao, Y.; Gao, Y.; Fu, W.; Han, Y. Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking. Sensors 2018, 18, 4416.

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