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Study on multiple targets tracking algorithm based on multiple sensors

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Abstract

For the problem that traditional data association algorithms tend to coalesce neighboring tracks for multiple close targets tracking application in dense clutter, measurements adaptive censor (MAC) method to Set JPDA (SJPDA) algorithm was introduced in this paper, then the proposed the MACSJPDA algorithm of target tracking discards several data associations with small probability and accelerates the convergence speed of the SJPDA algorithm. The algorithm can achieve better effects of multiple targets tracking by multiple sensors in wireless sensor networks. Monte Carlo simulation revealed that estimation effect of the MACSJPDA algorithm is much smoother, and it needs less run time than SJPDA algorithm for handling closely spaced and crossing targets, in the meanwhile the mean optimal sub-pattern assignment (MOSPA) deviation of the MACSJPDA algorithm is also smaller.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (11574120, U1636117), the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, China (UASP1503), the Natural Science Foundation of Jiangsu Province of China (BK20161359). Foundation of Key Laboratory of Underwater Acoustic Warfare Technology of China and Qing Lan Project.

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Authors and Affiliations

  1. College of Electronics and Information, Jiangsu University of Science and Technology, Mengxi Road No. 2, Zhenjiang, 212003, China

    Biao Wang, Kelei Feng, Wenzhong Yang & Zhiyu Zhu

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  1. Biao Wang
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  2. Kelei Feng
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  3. Wenzhong Yang
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  4. Zhiyu Zhu
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Correspondence to Biao Wang.

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Wang, B., Feng, K., Yang, W. et al. Study on multiple targets tracking algorithm based on multiple sensors. Cluster Comput 22 (Suppl 6), 13283–13291 (2019). https://doi.org/10.1007/s10586-018-1846-3

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