TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Robust Generalized Labeled MultiBernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian |
| 영문제목(English Title) |
Robust Generalized Labeled MultiBernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian |
| 저자(Author) |
Peng Li
Wenhui Wang
Junda Qiu
Congzhe You Zhenqiu Shu
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| 원문수록처(Citation) |
VOL 16 NO. 03 PP. 0908 ~ 0928 (2022. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English 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. |
| 키워드(Keyword) |
GLMB
target tracking
trajectory smoothing
TDVB
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| 파일첨부 |
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