Cluster Computing

, Volume 22, Supplement 6, pp 13283–13291 | Cite as

Study on multiple targets tracking algorithm based on multiple sensors

  • Biao WangEmail author
  • Kelei Feng
  • Wenzhong Yang
  • Zhiyu Zhu


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.


JPDA Multiple targets tracking Sensor networks Target tracking 



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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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electronics and InformationJiangsu University of Science and TechnologyZhenjiangChina

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