{"title":"Distributed Self Localisation of Sensor Networks using Particle Methods","authors":"N. Kantas, Sumeetpal S. Singh, A. Doucet","doi":"10.1109/NSSPW.2006.4378845","DOIUrl":null,"url":null,"abstract":"We describe how a completely decentralized version of Recursive Maximum Likelihood (RML) can be implemented in dynamic graphical models through the propagation of suitable messages that are exchanged between neighbouring nodes of the graph. The resulting algorithm can be interpreted as a generalization of the celebrated belief propagation algorithm to compute likelihood gradients. This algorithm is applied to solve the sensor localisation problem for distributed trackers forming a sensor networks. An implementation is given for dynamic nonlinear model without loops using Sequential Monte Carlo (SMC) or particle","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSPW.2006.4378845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
We describe how a completely decentralized version of Recursive Maximum Likelihood (RML) can be implemented in dynamic graphical models through the propagation of suitable messages that are exchanged between neighbouring nodes of the graph. The resulting algorithm can be interpreted as a generalization of the celebrated belief propagation algorithm to compute likelihood gradients. This algorithm is applied to solve the sensor localisation problem for distributed trackers forming a sensor networks. An implementation is given for dynamic nonlinear model without loops using Sequential Monte Carlo (SMC) or particle
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