Bayesian target tracking based on particle filter

被引：7

作者：

邓小龙

谢剑英

郭为忠

机构：

[1] Dept of Automation

[2] Shanghai Jiaotong Univ

[3] Dept of Mechanical Engineering

[4] Shanghai Jiaotong Univ Shanghai

[5] P R China

[6] Shanghai

来源：

JournalofSystemsEngineeringandElectronics | 2005年 / 03期

关键词：

nonlinear/non Gaussian; extended Kalman filter; particle filter; target tracking; proposal function;

D O I：

暂无

中图分类号：

TN953 [雷达跟踪系统];

学科分类号：

080904 ; 0810 ; 081001 ; 081002 ; 081105 ; 0825 ;

摘要：

For being able to deal with the nonlinear or non Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.

引用

页码：545 / 549

页数：5