Effective implementation and improvement of fast labeled multi-Bernoulli filter

doi:10.23919/JSEE.2023.000030

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (3): 661-673.doi: 10.23919/JSEE.2023.000030

• ELECTRONICS TECHNOLOGY • Previous Articles

Effective implementation and improvement of fast labeled multi-Bernoulli filter

Xuan CHENG(), Hongbing JI(), Yongquan ZHANG()

¹ School of Electronic Engineering, Xidian University, Xi’an 710071, China

Received:2021-08-09 Online:2023-06-15 Published:2023-06-30
Contact: Hongbing JI E-mail:chengxuanxd@163.com;hbji@xidian.edu.cn;zhangyq@xidian.edu.cn
About author:
CHENG Xuan was born in 1991. He received his M.S. degree from Xidian University. He is a Ph.D. degree candidate at the School of Electronic Engineering, Xidian University. His research interests include target tracking and nonlinear filtering. E-mail: chengxuanxd@163.com

JI Hongbing was born in 1963. He received his Ph.D. degree from Xidian University. He is a professor and doctoral supervisor of Xidian University. His research interests are intelligent information processing, modern signal processing, image processing, target tracking and recognition, and multi-sensor information fusion. E-mail: hbji@xidian.edu.cn

ZHANG Yongquan was born in 1985. He received his Ph.D. degree from Xidian University. He is an associate professor and master supervisor of Xidian University. His research interests include target detection and tracking, pattern recognition and classification, machine learning and nonlinear filtering. E-mail: zhangyq@xidian.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61871301), the Postdoctoral Science Foundation of China (2018M633470; 2020T130494), and the Fundamental Research Funds for the Central Universities (XJS210211)

Abstract

Abstract:

Effective implementation of the fast labeled multi-Bernoulli (FLMB) filter is addressed for target tracking with interval measurements. Firstly, a sequential Monte Carlo (SMC) implementation of the FLMB filter, SMC-FLMB filter, is derived based on generalized likelihood function weighting. Then, a box particle (BP) implementation of the FLMB filter, BP-FLMB filter, is developed, with a computational complexity reduction of the SMC-FLMB filter. Finally, an improved version of the BP-FLMB filter, improved BP-FLMB (IBP-FLMB) filter, is proposed, improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter. Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter, with similar tracking performance. Compared with the BP-FLMB filter, the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.

Key words: multi-target tracking, interval measurements, fast labeled multi-Bernoulli (FLMB) filter, sequential Monte Carlo (SMC) implementation, box particle (BP) implementation

Xuan CHENG, Hongbing JI, Yongquan ZHANG. Effective implementation and improvement of fast labeled multi-Bernoulli filter[J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 661-673.

Figures/Tables 8

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Effective implementation and improvement of fast labeled multi-Bernoulli filter

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