A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

FAN Jiande; XIE Weixin; LIU Zongxiang

doi:10.23919/cje.2021.00.282

Volume 32 Issue 3

May 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(3): 429-437

FAN Jiande, XIE Weixin, LIU Zongxiang, “A Low Complexity Distributed Multitarget Detection and Tracking Algorithm,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 429-437, 2023, doi: 10.23919/cje.2021.00.282

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FAN Jiande, XIE Weixin, LIU Zongxiang, “A Low Complexity Distributed Multitarget Detection and Tracking Algorithm,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 429-437, 2023, doi: 10.23919/cje.2021.00.282

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A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

doi: 10.23919/cje.2021.00.282

FAN Jiande^{1, 2
,},
XIE Weixin^{1, 2
,},
LIU Zongxiang^{1, 2
,}

1.
1. ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China
2.
2. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China

Funds: This work was supported by the National Natural Science Foundation of China (61271107, 61703280) and the Shenzhen Basic Research Project (JCYJ20170818102503604)

More Information

Author Bio:
Jiande FAN was born in 1985. He is currently pursuing the Ph.D. degree in College of Electronic and Information Engineering at Shenzhen University. His main research interests are multi-sensor multi-target tracking and intelligent information processing. (Email: jdfan@szu.edu.cn)

Weixin XIE was born in 1941. He received the B.S. degree from Xidian University, Xi’an, China. He is currently a Professor with Shenzhen University. His main research interests are intelligent information processing, fuzzy information processing, image processing, and pattern recognition. (Email: wxxie@szu.edu.cn)

Zongxiang LIU (corresponding author) was born in 1965. He received the Ph.D. degree from Xidian University, Xi’an, China. He is currently a Professor with Shenzhen University, China. His research interests include multi-sensor information fusion, multiple target tracking, and random set theory. (Email: liuzx@szu.edu.cn)
Received Date: 2021-08-10
Accepted Date: 2021-11-25

Available Online: 2022-03-24

Publish Date: 2023-05-05

Abstract

Abstract

In this paper, we propose a low complexity distributed approach to address the multitarget detection/tracking problem in the presence of noisy and missing data. The proposed approach consists of two components: a distributed flooding scheme for measurements exchanging among sensors and a sampling-based clustering approach for target detection/tracking from the aggregated measurements. The main advantage of the proposed approach over the prevailing Markov-Bayes-based distributed filters is that it does not require any priori information and all the information required is the measurement set from multiple sensors. A comparison of the proposed approach with the available distributed clustering approaches and the cutting edge distributed multi-Bernoulli filters that are modeled with appropriate parameters confirms the effectiveness and the reliability of the proposed approach.
- Sensor fusion,
- Distributed multitarget tracking,
- Constrained clustering,
- Flooding,
- Average consensus

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References(29)

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