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

作者：Zhu Hao^*; Leung Henry; He Zhongshi

来源：Information Sciences, 2013, 221: 201-214.

DOI：10.1016/j.ins.2012.09.017

• Sensor fusion
• Outliers
• Student-t distribution
• Robust data fusion
• Variational Bayesian

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.

• 出版日期2013-2-1
• 单位重庆大学