文章基本信息

标题：Iterative Truncated Unscented Particle Filter
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作者：Yanbo Wang ; Fasheng Wang ; Jianjun He 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：4
页码：214-228
DOI：10.3390/info11040214
出版社：MDPI Publishing
摘要：The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.
关键词：state estimation; particle filter; iterative unscented Kalman filter; iterative truncated particle filter state estimation ; particle filter ; iterative unscented Kalman filter ; iterative truncated particle filter