杂波环境下基于全邻模糊聚类的联合概率数据互联算法

doi:10.11999/JEIT150849

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Abstract

This paper proposes a new Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering (ANFCJPDA) for mutitarget tracking in the clutter. Firstly, distance measure is established according to measurements distribution in validation area and data correlation rules. Then, the predicted position is set up as a cluster center, and the association probabilities are calculated on the basis of fuzzy clustering, which are used as weights to update targets’ state and the covariance. Simulation results show that the proposed method reduces highly the computational complexity compared to conventional Joint Probabilistic Data Association (JPDA) technique, and is effective for multiple target tracking in a cluttered environment.

Key words： Multiple target tracking Multisensor Data association Fuzzy clustering

Received: 16 July 2015 Published: 07 April 2016

PACS:	TN953
	TN957

Fund:

The National Natural Science Foundation of China (61471383)

Corresponding Authors: LIU Jun E-mail: 18615042187@163.com

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	LIU Jun
	LIU Yu
	HE You
	SUN Shun

Cite this article:

LIU Jun,LIU Yu,HE You等. Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter[J]. JEIT, 2016, 38(6): 1438-1445.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT150849 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I6/1438

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