Eztension Particle Filtering Algorithm for State and Parameter Estimation in Dynamic Control Process
Aiming to solve the problem of unknown parameters estimation in nonlinear and/or non-Gaussian dynamic system, Extension Particle Filtering(EPF) algorithm was proposed. EPF algorithm modeled unknown parameters by Gaussian random walk process, regarded unknown parameters in dynamic system as a part of state variations, and then estimated the state variations in extension nonlinear dynamic system by particle filtering algorithm. In order to improve the estimate precision of unknown parameters by utilizing observable information effectively, a new important density was purposed to instead of Bootstrap filter, further more, it avoided transformation about covariance. In order to solve the problem that covariance augmented infinitely with time in Gaussian random walk model, and Kernel smooth factor restrained covariance excessively so as to the values of estimate parameters could not access the values of true parameters sufficiently, the Gradually Reduce(GR) factor was purposed to instead of Kernel factor. At the end of this paper, the effectiveness and availability of purposed algorithm was validated by an exemplum.
Particle Filtering algorithm Parameters estimation Important density Smooth factor
GAO Xian-zhong HOU Zhong-xi REN Bo-tao
College of aerospace and material engineering, National University of Defense Technology, Changsha 410073
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
1133-1137
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）