基于t分布混合模型的抗差关联算法

doi:10.11999/JEIT161084

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Abstract

In order to solve the problem of robust track-to-track association in the presence of sensor biases and non-identical observation, an anti-bias track association algorithm based on t-distribution mixture model is proposed. The robust track-to-track association problem is turned into the non-rigid point matching problem. The tracks of non-common are regarded as outliers in the point matching for the effects of the track-to-track association caused by non-identical observation. The heavy-tailed t-distribution mixture model is established with better robustness to outliers. The closed-form solution of t-distribution mixture model is solved by Expectation Maximization (EM) algorithm. The conditional expectation function is added a regular item of point set, so that the points have a feature of Coherent Point Drift (CPD). Finally, the effectiveness of the proposed algorithm is verified by simulation experiments at the presence of sensor biases and missed detections.

Key words： Track association Sensor biases t-distribution mixture model Expectation Maximization (EM) algorithm Coherent Point Drift (CPD)

Received: 14 October 2016 Published: 14 April 2017

PACS:

TN957

Fund:

The National Natural Science Foundation of China (61471382, 61401495, 61501487, 61531020), The Natural Science Foundation of Shandong Province (2015ZRA06052)

Corresponding Authors: GUAN Jian E-mail: guanjian96@tsinghua.org.cn

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	LI Baozhu
	DONG Yunlong
	LI Xiuyou
	GUAN Jian

Cite this article:

LI Baozhu,DONG Yunlong,LI Xiuyou等. Anti-bias Track Association Algorithm Based on t-distribution Mixture Model[J]. JEIT, 2017, 39(7): 1774-1778.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT161084 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I7/1774

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