【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 extend
【机 构】
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Dept .of Automation,Shanghai Jiaotong Univ,Dept .of Automation,Shanghai Jiaotong Univ,Dept .of Mecha
【出 处】
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Journal of Systems Engineering and Electronics
论文部分内容阅读
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.
For be 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.
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