Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments

Nemeth, Christopher and Fearnhead, Paul and Mihaylova, Lyudmila (2014) Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments. IEEE Transactions on Signal Processing, 62 (5). pp. 1245-1255. ISSN 1053-587X

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Abstract

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 Bayesian parameter estimation techniques. The developed approach is validated over complex maneuvering scenarios where both the system parameters and measurement noise parameters are unknown. Accurate estimation results are presented.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Signal Processing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
68620
Deposited By:
Deposited On:
17 Feb 2014 09:51
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Sep 2020 02:05