Adaptive collaborative Gaussian mixture probability hypothesis density filter for multi-target tracking

Feng Yang, Yongqi Wang, Hao Chen, Pengyan Zhang, Yan Liang

科研成果: 期刊稿件文章同行评审

4 引用 (Scopus)

摘要

In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.

源语言英语
文章编号1666
期刊Sensors
16
10
DOI
出版状态已出版 - 11 10月 2016

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