基于蒙特卡洛方法的高斯混合采样粒子滤波算法研究
摘要
本文提出了一种标准粒子滤波器的改进算法——高斯混合采样粒子滤波算法(GMSPPF)。仿真结果表明,新算法在大幅降低计算复杂度的前提下,具有比标准粒子滤波算法(SIR-PPF)更好估计性能.
出处
《计算机与信息技术》
2008年第Z1期32-35,共4页
Computer & Information Technology
参考文献10
-
1Y.C.Ho,and R.C.K.Lee."A Bayesian approach to problems in stochastic estimation and control"[].IEEE Transactions on Automatic Control.
-
2A.Doucet,N.Freitas,N.Gordon.Sequential Monte Carlo Methods in Practice[]..
-
3B.D.O.Anderson,and J.B.Moore.Optimal filtering.[]..1979
-
4MULLER."Monte Carlo integration in general dynamic Models"[].Contemporary Mathematics.1991
-
5Fredric Gustafsson,Niclas Bergman."Particle filters for Position,Navigation and Tracking"[].Final version for IEEE Transactions on Signal Processing Special issue on Monte Carlo methods for statistical signal.
-
6Jayesh H Kotecha,and Petar M.Djurric."Gaussian Particle Filtering"[].ProcWorkshop Statistical Signal Process.2001
-
7ZHE CHEN."Bayesian filtering:From Kalman filter to particle filters,And Beyond"[]..2003
-
8Jayesh H.Kotecha,and Petar M.Djurric."Gaussian Sum Particle Filtering"[].IEEE Transactions on Signal Processing.2003
-
9M.Sanjeev Arulampalam,Simon Maskell,Neil Gordon,and Tim Clapp."A Tutorial on Particle Filteolinear/Non-Gaussian Bayesian Tracking"[].IEEE Transactions on Signal Processing.2002
-
10Gordon,N.J.,Salmond,D.J.,Smith,A.F.M.Novel approach to nonlinear/non-Gaussian Bayesian state estimation[].IEE Proceedings F Radar and Signal Processing.1993
