Volume 32 Issue 3
May  2023
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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
Citation: 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

A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

doi: 10.23919/cje.2021.00.282
Funds:  This work was supported by the National Natural Science Foundation of China (61271107, 61703280) and the Shenzhen Basic Research Project (JCYJ20170818102503604)
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  • 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
  • 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.
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