杂波环境下基于最大熵模糊聚类的JPDA算法

doi:10.12305/j.issn.1001-506X.2023.07.02

Abstract

Abstract:

Aiming at the problems of low tracking accuracy and poor real-time performance of multi-target tracking data association in clutter environment, this paper proposes a joint probabilistic data association algorithm based on maximum entropy fuzzy clustering (MEFC-JPDA). Firstly, the membership obtained by the maximum entropy fuzzy clustering is used to preliminarily characterize the correlation probability between the target and the effective measurement. Secondly, the measurement correction factor based on target distance is used to adjust the correlation probability, and the correlation probability matrix is established. Finally, combined with the Kalman filtering algorithm, the state of the target is weighted updated. Simulation results show that the tracking performance of the proposed algorithm in clutter environment is greatly improved compared with the existing two association algorithms, and it is an effective multi-target tracking data association algorithm.

Key words: multi-target tracking, joint probability data association (JPDA), maximum entropy fuzzy clustering (MEFC), measurement correction factor

CLC Number:

TN953

Wenhao BI, Jie ZHOU, An ZHANG, Li LIU. JPDA algorithm based on maximum entropy fuzzy clustering in clutter environment[J]. Systems Engineering and Electronics, 2023, 45(7): 1920-1927.

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目标编号	位置/m		速度/(m·s^-1)
目标编号	x_k(r)	y_k(r)	$\dot{x}_k(r)$	$\dot{y}_k(r)$
A	600	1 200	215	50
B	600	3 600	215	50

目标编号	位置/m		速度/(m·s^-1)
目标编号	x_k(r)	y_k(r)	$\dot{x}_k(r)$	$\dot{y}_k(r)$
A	500	1 100	235	125
B	600	2 200	255	85

杂波密度	传统JPDA算法	本文算法	文献[15]
λ=1	3.006	2.122	2.105
λ=6	5.648	2.424	2.514

杂波密度	传统JPDA算法	本文算法	文献[15]
λ=1	3.065	2.145	2.028
λ=6	5.329	2.201	2.195

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JPDA algorithm based on maximum entropy fuzzy clustering in clutter environment

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