Particle Swarm Optimized Unscented Particle Filter for Target Tracking

被引:0
作者
Yang, Shuying [1 ]
Ma, Qin [1 ]
Huang, Wenjuan [1 ]
机构
[1] Tianjin Univ Technol, Tianjin Key Lab Intelligence Comp & Novel Softwar, Tianjin 300191, Peoples R China
来源
PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9 | 2009年
关键词
Unscented Particle Filter; Particle Swarm Optimization; Particle Impoverishment; Video Tracking;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a novel particle swarm optimized (PSO) unscented particle filter (PSO-UPF) algorithm is proposed for target tracking. Unscented particle filter (UPF) can obtain the better sequential importance sampling than the traditional PF algorithm. Then we use PSO to optimize the state equation of UPF. So that the particle set can tend to the high likelihood region before the weights updated, thereby the impoverishment of particles can be overcome. While, the optimization process makes the particles which far away from the true state tend to the region where the true state has a greater probability of emergence, it can enhance the effect of each particle. Experiment results show that our modified particle filter algorithm uses fewer particles than the general particle filters and its performance outperforms them. And the accuracy of video tracking is improved.
引用
收藏
页码:2071 / 2075
页数:5
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