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An Interacting Multiple Model Approach for State Estimation with Non‐Gaussian Noise Using a Variational Bayesian Method
Author(s) -
Shen Chen,
Xu Dingjie,
Huang Wei,
Shen Feng
Publication year - 2015
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1055
Subject(s) - outlier , gaussian , noise (video) , bayesian probability , algorithm , gaussian noise , computer science , noise measurement , bayesian inference , mathematics , artificial intelligence , pattern recognition (psychology) , noise reduction , physics , quantum mechanics , image (mathematics)
Abstract The conventional interacting multiple models (IMM) approach for a hybrid system under the Gaussian assumption is limited for most real applications due to the noisy measurements often being in the presence of outliers. This paper aims at accommodating the IMM approach to the non‐Gaussian cases where outliers exist. In the proposed IMM algorithm, the Student‐t distribution is used to model the non‐Gaussian measurement noise. At the interaction step, the mixed statistics of the noise parameter under a Bayesian framework are obtained via a Gamma approximation and a recently reported moments matching method. To address the state noise‐coupled intractability in Bayesian filtering, a variational Bayesian method is utilized to approximate the posterior distributions of the noise and state recursively. The proposed algorithm is tested with a maneuvering target tracking example and is shown to be robust to the outliers.