CASIA OpenIR  > 09年以前成果
A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem
Pang, Zhonyu1; Liu, Derong1,2; Jin, Ning1; Wang, Zhuo1
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
2008-12-01
Volume55Issue:11Pages:3421-3429
SubtypeArticle
AbstractSequential 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.
KeywordBackpropagation Neural Networks Particle Degeneracy Particle Filters Resample-move Tracking
WOS HeadingsScience & Technology ; Technology
WOS KeywordSTATE ESTIMATION ; SEQUENTIAL IMPUTATIONS ; BAYESIAN-INFERENCE ; DYNAMIC-SYSTEMS ; FILTERS ; SIMULATION ; ALGORITHM ; CHANNELS
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000261847400006
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9533
Collection09年以前成果
Affiliation1.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
Recommended Citation
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.
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