文章基本信息

标题：Improving the particle filter in high dimensions using conjugate artificial process noise
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作者：Anna Wigren ; Lawrence Murray ; Fredrik Lindsten 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：670-675
DOI：10.1016/j.ifacol.2018.09.207
语种：English
出版社：Elsevier
摘要：AbstractThe particle filter is one of the most successful methods for state inference and identification of general non-linear and non-Gaussian models. However, standard particle filters suffer from degeneracy of the particle weights, in particular for high-dimensional problems. We propose a method for improving the performance of the particle filter for certain challenging state space models, with implications for high-dimensional inference. First we approximate the model by adding artificial process noise in an additional state update, then we design a proposal that combines the standard and the locally optimal proposal. This results in a bias-variance tradeoff, where adding more noise reduces the variance of the estimate but increases the model bias. The performance of the proposed method is empirically evaluated on a linear-Gaussian state space model and on the non-linear Lorenz’96 model. For both models we observe a significant improvement in performance over the standard particle filter.
关键词：KeywordsData assimilationSequential Monte CarloEstimationfilteringState-space modelsNonlinear system identification