Bayesian target tracking based on particle filter

作     者：邓小龙 谢剑英 郭为忠 

作者机构：Dept .of AutomationShanghai Jiaotong Univ Dept .of Mechanical EngineeringShanghai Jiaotong Univ 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2005年第16卷第3期

页      面：545-549页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081105[工学-导航、制导与控制] 0802[工学-机械工程] 081001[工学-通信与信息系统] 081002[工学-信号与信息处理] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This project was supported by the National Natural Science Foundation of China (50405017) 

主　　题：nonlinear/non-Gaussian extended Kalman filter particle filter target tracking proposal function. 

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, ere 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.