Optimization of particle CBMeMBer filters for hardware implementation

Chaoqun Yang; Zhiguo Shi; Kuan Han; Jun Jason Zhang; Yujie Gu; Zhongya Qin

doi:10.1109/tvt.2018.2853120

Optimization of particle CBMeMBer filters for hardware implementation

Chaoqun Yang, Zhiguo Shi, Kuan Han, Jun Jason Zhang, Yujie Gu, Zhongya Qin

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

It is a promising solution for real-time multitarget tracking to implement particle cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filters in hardware platforms. However, this solution is difficult to materialize since there is a contradiction between the time-varying number of Bernoulli intensity components in CBMeMBer filters and the limited number of particles in hardware platforms. Moreover, real-time hardware implementation requires a resampling procedure that is suitable for parallel processing, while the existing parallel resampling algorithms oversimplify this procedure, resulting in estimation performance degradation. In this paper, we propose an optimization algorithm of particle allocation to overcome the above-mentioned contradiction, and a parallel resampling algorithm to improve the estimation performance. Numerical experiments demonstrate the effectiveness of the proposed algorithms in multitarget tracking.

Original language	English
Article number	8404103
Pages (from-to)	9027-9031
Number of pages	5
Journal	IEEE Transactions on Vehicular Technology
Volume	67
Issue number	9
DOIs	https://doi.org/10.1109/tvt.2018.2853120
State	Published - Sep 2018
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 1967-2012 IEEE.

Funding

Manuscript received April 7, 2018; revised May 30, 2018; accepted June 26, 2018. Date of publication July 5, 2018; date of current version September 17, 2018. This work was partially supported by NSFC under Grant 61772467, and in part by Zhejiang Provincial Natural Science Foundation of China under Grant LR16F010002 and 973 Project under Grant 2015CB352503. The review of this paper was coordinated by Dr. H. Lin. This paper was presented in part at IEEE 10th International Conference on Information, Communications and Signal Processing, Singapore, December 2015. (Corresponding Author: Zhiguo Shi.) C. Yang, Z. Shi, K. Huan, and Z. Qin are with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China (e-mail:,[email protected]; [email protected]; [email protected]; [email protected]).

Funders	Funder number
973 Project	2015CB352503
Zhejiang Provincial Natural Science Foundation of China	LR16F010002
National Natural Science Foundation of China	61772467

Keywords

Advanced driver-assistance systems
cardinality balanced multi-target multi-Bernoulli filters
hardware implementation
parallel resampling
particle allocation

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10.1109/tvt.2018.2853120

Cite this

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abstract = "It is a promising solution for real-time multitarget tracking to implement particle cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filters in hardware platforms. However, this solution is difficult to materialize since there is a contradiction between the time-varying number of Bernoulli intensity components in CBMeMBer filters and the limited number of particles in hardware platforms. Moreover, real-time hardware implementation requires a resampling procedure that is suitable for parallel processing, while the existing parallel resampling algorithms oversimplify this procedure, resulting in estimation performance degradation. In this paper, we propose an optimization algorithm of particle allocation to overcome the above-mentioned contradiction, and a parallel resampling algorithm to improve the estimation performance. Numerical experiments demonstrate the effectiveness of the proposed algorithms in multitarget tracking.",

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Optimization of particle CBMeMBer filters for hardware implementation

Abstract

Bibliographical note

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Keywords

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