Multi-sensor GIW-PHD filter for multiple extended target tracking

被引：0

作者：

Li, Peng ^{[1
]}

Yang, Jinlong ^{[1
]}

Tie, Hongwei ^{[1
]}

Zhang, Huanqing ^{[1
]}

机构：

[1] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China

来源：

2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2015年

关键词：

Multiple extended target tracking; Multi-sensor; Inverse Wishart; Probability hypothesis density;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has proven to be a promising algorithm for multiple extended target tracking with shape estimation. However, as far as I know, this method only can be used in the single sensor tracking system, which cannot obtain the accurate state estimates for the complex tracking scenario. To solve this problem, we propose a multi-sensor GIW-PHD method by using the multiple sensor infusion technique, which is suitable to the multi-sensor tracking system for multiple extended target tracking. First, a novel measurement model of the extended target is constructed for multi-sensor in three-dimensional scenario, and then the fusion formulas of state update are derived. Simulation results show that the proposed algorithm has a better performance than that of the conventional GIW-PHD with a single sensor.

引用

页码：5620 / 5625

页数：6

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