A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem

CASIA OpenIR > 09年以前成果

	A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem
	Pang, Zhonyu 1; Liu, Derong 1,2; Jin, Ning 1; Wang, Zhuo1
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
	2008-12-01
卷号	55 期号:11 页码:3421-3429
文章类型	Article
摘要	Sequential Monte Carlo (SMC) methods, namely, particle filters, are powerful simulation techniques for Sampling sequentially from a complex probability distribution. SMC can be used to solve some problems associated with nonlinear non-Gatissian probability distribution. Sampling is a key step for these methods and has vital effects on simulation results. Various sampling strategies have been proposed to improve the simulation results of SMC methods, but degeneracy of particles sometimes is very severe so that there are only a few particles having significant weights. Diversity of particle samples is reduced significantly so that only a few particles are used to represent the corresponding probability distribution. This kind of sampling is not reasonable to approximate probability distribution. This paper addresses a new method which can avoid the phenomenon of particle degeneracy. We split particles with very big weights into two small ones and use the strategy of neural network to adjust positions of tail particles in order to increase their weights. Another advantage is that this method can efficiently make simulation results approach the actual object. Our simulation results of the typical tracking problem show that not only the phenomenon of particle degeneracy is effectively avoided but also tracking results are much better titan those of the traditional particle filters. Compared with the move-resample method, our method shows better results under the same conditions.
关键词	Backpropagation Neural Networks Particle Degeneracy Particle Filters Resample-move Tracking
WOS标题词	Science & Technology ; Technology
关键词[WOS]	STATE ESTIMATION ; SEQUENTIAL IMPUTATIONS ; BAYESIAN-INFERENCE ; DYNAMIC-SYSTEMS ; FILTERS ; SIMULATION ; ALGORITHM ; CHANNELS
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000261847400006
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/9583
专题	09年以前成果
作者单位	1.Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA 2.Chinese Acad Sci, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Pang, Zhonyu,Liu, Derong,Jin, Ning,et al. A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS,2008,55(11):3421-3429.
APA	Pang, Zhonyu,Liu, Derong,Jin, Ning,&Wang, Zhuo.(2008).A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS,55(11),3421-3429.
MLA	Pang, Zhonyu,et al."A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 55.11(2008):3421-3429.