Multi-target tracking algorithm based on kernel density estimation Gaussian mixture PHD filter

被引：0

作者：

Zhou W.-D. ^{[1
]}

Zhang H.-B. ^{[1
]}

Qiao X.-W. ^{[1
]}

机构：

[1] College of Automation, Harbin Engineering University

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2011年 / 33卷 / 09期

关键词：

Mean-shift algorithm; Multi-target tracking; Probability hypothesis density (PHD); Random set;

D O I：

10.3969/j.issn.1001-506X.2011.09.04

中图分类号：

学科分类号：

摘要：

Considering the lower estimated accuracy of traditional algorithms in multi-target tracking system, a Gaussian mixture probability hypothesis density (PHD) filtering algorithm based on kernel density estimation is proposed. After pruning and merging in this algorithm, the Mean-shift algorithm is introduced to estimate kernel density of Gaussian mixture PHD distribution density function, which replaces the traditional state estimation methods. Finally, the estimated peak value is used as the state value. Simulation results show that compared with the traditional algorithms, the proposed algorithm has a higher tracking accuracy.

引用

页码：1932 / 1936

页数：4

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