作者
Christopher Nemeth, Paul Fearnhead, Lyudmila Mihaylova
发表日期
2013/12/23
期刊
IEEE Transactions on Signal Processing
卷号
62
期号
5
页码范围
1245-1255
出版商
IEEE
简介
This paper develops a novel sequential Monte Carlo (SMC) approach for joint state and parameter estimation that can deal efficiently with abruptly changing parameters which is a common case when tracking maneuvering targets. The approach combines Bayesian methods for dealing with change-points with methods for estimating static parameters within the SMC framework. The result is an approach that adaptively estimates the model parameters in accordance with changes to the target's trajectory. The developed approach is compared against the Interacting Multiple Model (IMM) filter for tracking a maneuvering target over a complex maneuvering scenario with nonlinear observations. In the IMM filter a large combination of models is required to account for unknown parameters. In contrast, the proposed approach circumvents the combinatorial complexity of applying multiple models in the IMM filter through …
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C Nemeth, P Fearnhead, L Mihaylova - IEEE Transactions on Signal Processing, 2013