文章基本信息

标题：Learning Nonlinear State-Space Models Using Smooth Particle-Filter-Based Likelihood Approximations ⁎
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作者：Andreas Svensson ; Fredrik Lindsten ; Thomas B. Schön 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：652-657
DOI：10.1016/j.ifacol.2018.09.216
语种：English
出版社：Elsevier
摘要：AbstractWhen classical particle filtering algorithms are used for maximum likelihood parameter estimation in nonlinear state-space models, a key challenge is that estimates of the likelihood function and its derivatives are inherently noisy The key idea in this paper is to run a particle filter based on a current parameter estimate, but then use the output from this particle filter to re-evaluate the likelihood function approximation also for other parameter values. This results in a (local) deterministic approximation of the likelihood and any standard optimization routine can be applied to find the maximum of this approximation. By iterating this procedure we eventually arrive at a final parameter estimate.
关键词：KeywordsSystem identificationparameter estimationnonlinear state-space modelsparticle filtermaximum likelihoodoptimization