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	State tracking measurement method using particle filter based on radial basis function neural network
	Wang Xue ; Wang Sheng ; Ma Junjie
	2010-05-10 ; 2010-05-10
关键词	Theoretical or Mathematical/ adaptive filters Kalman filters particle filtering (numerical methods) radial basis function networks statistical distributions target tracking/ radial basis function neural network particle filter state tracking measurement method self-adaptive filter tracking unscented particle filter unscented Kalman filter posterior probability distribution estimation error radial basis function network RBFN sampling process target tracking intrinsic property/ B6140B Filtering methods in signal processing B0240Z Other topics in statistics C5260 Digital signal processing C1260S Signal processing theory C5290 Neural computing techniques C1140Z Other topics in statistics
中文摘要	In statement tracking, drastic change increases the process noise and accordingly increases the difficulty of self-adaptive filter tracking. Traditional particle filter algorithm has a disadvantage that if change is too drastic, it can not correct errors effectively, which makes the estimation errors cumulate and the tracking system become divergent. Unscented particle filter (UPF) algorithm, which uses an unscented Kalman filter (UKF) for proposal distribution generation within a particle filter framework, can decrease the posterior probability distribution estimation error, enhance tracking effect, but it also increase the computation time. An improved particle filter algorithm (PF-RBF) based on radial basis function network (RBFN) is proposed, which aims at improving the sampling process of new particles and reducing the computation time. The algorithm uses RBFN to construct the process model dynamically from the observations and update the state of the system, which can reduce prior probability distribution estimation error and remove the cumulated effect of errors. Compared with UPF, PF-RBF can reduce computation time because it doesn't contain UKF process. The target tracking experiment results verify that PF-RBF performs better than UKF, PF and UPF whether the observation model is nonlinear or linear. Furthermore, the intrinsic property of PF-RBF determines that the change rate of execution time of PF-RBF is less than UPF, so PF-RBF is more suitable for large-scale applications.
语种	中文 ; 中文
出版者	Chinese J. Mech. Eng ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/24409]
专题	清华大学
推荐引用方式 GB/T 7714	Wang Xue,Wang Sheng,Ma Junjie. State tracking measurement method using particle filter based on radial basis function neural network[J],2010, 2010.
APA	Wang Xue,Wang Sheng,&Ma Junjie.(2010).State tracking measurement method using particle filter based on radial basis function neural network..
MLA	Wang Xue,et al."State tracking measurement method using particle filter based on radial basis function neural network".(2010).

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