Bayesian target tracking based on particle filter  

作　　者：邓小龙 谢剑英 郭为忠 

机构地区：[1]Dept .of Automation,Shanghai Jiaotong Univ [2]Dept .of Mechanical Engineering,Shanghai Jiaotong Univ

出　　处：《Journal of Systems Engineering and Electronics》2005年第3期545-549,共5页系统工程与电子技术（英文版）

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

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.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.

关 键 词：nonlinear/non-Gaussian extended Kalman filter particle filter target tracking proposal function. 

分 类 号：TN953[电子电信—信号与信息处理]