Adaptive Multi-Bernoulli Filter Without Need of Prior Birth Multi-Bernoulli Random Finite Set

YUAN Changshun; WANG Jun; LEI Peng; SUN Jinping

doi:10.1049/cje.2017.10.010

Volume 27 Issue 1

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Article Navigation > Chinese Journal of Electronics > 2018 > 27(1): 115-122

YUAN Changshun, WANG Jun, LEI Peng, et al. “Adaptive Multi-Bernoulli Filter Without Need of Prior Birth Multi-Bernoulli Random Finite Set”. Chinese Journal of Electronics, vol. 27 no. 1. doi: 10.1049/cje.2017.10.010

Citation:

YUAN Changshun, WANG Jun, LEI Peng, et al. “Adaptive Multi-Bernoulli Filter Without Need of Prior Birth Multi-Bernoulli Random Finite Set”. Chinese Journal of Electronics, vol. 27 no. 1. doi: 10.1049/cje.2017.10.010

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Adaptive Multi-Bernoulli Filter Without Need of Prior Birth Multi-Bernoulli Random Finite Set

doi: 10.1049/cje.2017.10.010

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61471019, No.61501011, No.61501012).

More Information

Corresponding author: WANG Jun (corresponding author) received the B.S. degree from the Northwestern Polytechnical University, Xi'an, China, in 1995 and the M.S. and Ph.D. degrees from the Beihang University, Beijing, China, in 1998 and 2001, respectively. He is currently a professor with the School of Electronic and Information Engineering, Beihang University. His research interests signal processing, DSP/FPGA realtime architecture, target recognition and tracking, etc. (Email:wangj203@buaa.edu.cn)
Received Date: 2015-10-22
Rev Recd Date: 2016-05-19
Publish Date: 2018-01-10

Abstract

Abstract

Conventional Multi-Bernoulli (MBer) filter assumes that the birth MBer Random finite set (RFS) is known a priori. However, this is not true for practical scenario. This paper proposes a novel extension of the MBer filter which eliminates the reliance of the prior birth MBer RFS and relaxes the limitation in new-born target appearance volume. The proposed filter classifies the measurements into survival measurements and birth measurements, and adaptively generates the birth MBer RFS using the birth measurements. The novel filtering equations that distinguish the persistent and new-born targets are derived. A Sequential Monte-Carlo (SMC) implementation of the proposed filter is given. Simulations are performed to verify the improvement in the performance of the proposed filter.
- Random finite set,
- Adaptive multiBernoulli filter,
- Sequential Monte-Carlo,
- Multi-target tracking

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References(24)

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