An Particle PHD Filter with Improved Resampling Design for Multiple Target Tracking

被引：1
|
作者：
Zeng Xiaohui [1 ,2 ]
Shi Yibing [1 ]
Zeng Xiaohui [1 ,2 ]
Lian Yi [3 ]
机构：
[1] UESTC, Sch Automat Engn, Chengdu, Peoples R China
[2] Chengdu Univ Informat & Technol, Sch Commun Engn, Chengdu, Peoples R China
[3] Motorola Solut China, Embedded Software Grp, Chengdu, Peoples R China
来源：
2014 IEEE 17TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) | 2014年
关键词：
multi-target tracking; partial resampling; probability hypothesis density (PHD) filter; random finite sets; IMPORTANCE SAMPLING FUNCTION;
D O I：
10.1109/CSE.2014.338
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Multi-target tracking is a complex problem including time-varying number of targets and their states in the presence of data association uncertainty and clutter. In this article, we develop a novel implementation of Sequential Monte Carlo filter with a new improved partial resampling strategy in random finite sets framework. This algorithm provides an approach to increase diversity of particles and keep accuracy of filtering performance. Simulation results verify that for the MTT problems, the proposed algorithm could achieve better performance than the standard particle PHD filter.
引用
收藏
页码：1844 / 1849
页数：6
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