Robust interacting multiple model algorithms based on multi-sensor fusion criteria

Weidong Zhou and Mengmeng Liu

International Journal of Systems Science, 2016, vol. 47, issue 1, 92-106

Abstract: This paper is concerned with the state estimation problem for a class of Markov jump linear discrete-time stochastic systems. Three novel interacting multiple model (IMM) algorithms are proposed based on the H∞ technique, the correlation among estimation errors of mode-conditioned filters and the multi-sensor optimal information fusion criteria. Mode probabilities in the novel algorithms are derived based on the error cross-covariances instead of likelihood functions. The H∞ technique taking the place of Kalman filtering is applied to enhance the robustness of the new approaches. Theoretical analysis and Monte Carlo simulation results indicate that the proposed algorithms are effective and have an obvious advantage in velocity estimation when tracking a maneuvering target.

Date: 2016
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DOI: 10.1080/00207721.2015.1029566

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