A variational Bayesian approach to robust sensor fusion based on Student-t distribution

Authors:
Hao Zhu;Henry Leung;Zhongshi He
Affiliations:
Department of Computer Science, Chongqing University, Chongqing 400044, PR China and Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada T2N 1N4 and ...;Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada T2N 1N4;Department of Computer Science, Chongqing University, Chongqing 400044, PR China
Venue:
Information Sciences: an International Journal
Year:
2013

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Abstract

In this paper, a robust sensor fusion method is proposed where the measurement noise is modeled by a Student-t distribution. The Student-t distribution has a heavy tail compared to the Gaussian distribution and is robust to outliers. We formulate sensor fusion as a state space estimation problem in the Bayesian framework. Both batch and recursive variational Bayesian (VB) algorithms are developed to perform this non-Gaussian state space estimation problem to obtain the fusion results. Computer simulations show that the proposed approach has an improved fusion performance and a lower computation cost compared to methods based on Gaussian and finite Gaussian mixture models.