• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian

Vol. 16, No. 3, March 31, 2022
10.3837/tiis.2022.03.009, Download Paper (Free):

Abstract

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets’ states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.


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Cite this article

[IEEE Style]
P. Li, W. Wang, J. Qiu, C. You and Z. Shu, "Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian," KSII Transactions on Internet and Information Systems, vol. 16, no. 3, pp. 908-928, 2022. DOI: 10.3837/tiis.2022.03.009.

[ACM Style]
Peng Li, Wenhui Wang, Junda Qiu, Congzhe You, and Zhenqiu Shu. 2022. Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian. KSII Transactions on Internet and Information Systems, 16, 3, (2022), 908-928. DOI: 10.3837/tiis.2022.03.009.

[BibTeX Style]
@article{tiis:25523, title="Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian", author="Peng Li and Wenhui Wang and Junda Qiu and Congzhe You and Zhenqiu Shu and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.03.009}, volume={16}, number={3}, year="2022", month={March}, pages={908-928}}