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A new smoothing algorithm for jump Markov linear systems
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DOI：10.1016/j.automatica.2022.110218.png)
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摘要
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This paper presents a method for calculating the smoothed state distribution for Jump Markov Linear Systems. More specifically, the paper details a novel two-filter smoother that provides closed-form expressions for the smoothed hybrid state distribution. This distribution can be expressed as a Gaussian mixture with a known, but exponentially increasing, number of Gaussian components as the time index increases. This is accompanied by exponential growth in memory and computational requirements, which rapidly becomes intractable. To ameliorate this, we limit the number of allowed mixture terms by employing a Gaussian likelihood mixture reduction strategy, which results in a computationally tractable, but approximate smoothed distribution. The approximation error can be balanced against computational complexity in order to provide an accurate and practical smoothing algorithm that compares favourably to existing state-of-the-art approaches. (c) 2022 Published by Elsevier Ltd.
Keyword:
Bayesian estimation
Smoothing
Jump Markov linear systems (JMLS)
Switched linear dynamical systems (SLDS)
Interacting multiple models (IMM)
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期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
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