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Open AccessArticle

A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking

1
National Key Laboratory of Science and Technology on ATR, College of Electronic Science, National University of Defense Technology, Changsha 410073, China
2
Key Laboratory of Information Fusion Technology (Ministry of Education), School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
*
Authors to whom correspondence should be addressed.
This paper is an extended and modified version of our conference paper “A Forward-Backward Labeled Multi-Bernoulli Smoother” published in Proceedings of the 16th International Conference on Distributed Computing and Artificial Intelligence, Avila, Spain, 26–28 June 2019.
Sensors 2019, 19(19), 4226; https://doi.org/10.3390/s19194226
Received: 8 September 2019 / Revised: 23 September 2019 / Accepted: 26 September 2019 / Published: 28 September 2019
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
A forward–backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches. View Full-Text
Keywords: random finite set; bayes smoother; labeled multi-Bernoulli; multi-target tracking; Sequential Monte Carlo random finite set; bayes smoother; labeled multi-Bernoulli; multi-target tracking; Sequential Monte Carlo
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MDPI and ACS Style

Liu, R.; Fan, H.; Li, T.; Xiao, H. A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking. Sensors 2019, 19, 4226.

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