标题:Added Markov Chain Monte Carlo particle filtering optimized by Mean Shift and application in target tracking
作者:Zhang, Pei ;Wang, Huiyuan ;Wang, Wen
通讯作者:Zhang, P.
作者机构:[Zhang, Pei ;Wang, Huiyuan ;Wang, Wen ] School of Information Science and Engineering, Shandong University, Jinan, Shandong 250100, China
会议名称:2011 IEEE 13th International Conference on Communication Technology, ICCT 2011
会议日期:25 September 2011 through 28 September 2011
来源:International Conference on Communication Technology Proceedings, ICCT
出版年:2011
页码:771-775
DOI:10.1109/ICCT.2011.6157981
关键词:AMCMC; Markov chain Monte Carlo; Mean Shift; particle filter
摘要:Bayesian statistics has attracted people's interest again recently because of the application of Markov Chain Monte Carlo (MCMC) theory. In particle filtering, the diversification of particles disappears in the process of importance sampling. However, this problem can be solved using Metropolis-Hastings (MH) sampling usually used in MCMC theory. As a modification to added MCMC (AMCMC)- an improved MCMC particle filter that can track variable number of targets at the same time, a new approach to optimize those rejected samples in MH sampling process by Mean Shift algorithm is proposed in this paper. Because the operation rate of particles in AMCMC is increased, the circles of sampling needed for the convergence of Markov chain is reduced. It is shown by experiment that, the optimized algorithm has better tracking performance under the condition of fewer particles. © 2011 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863235167&doi=10.1109%2fICCT.2011.6157981&partnerID=40&md5=5e04a716cb81cf43afdee7003c586923
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