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

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.

[1]  Simo Särkkä,et al.  Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations , 2009, IEEE Transactions on Automatic Control.

[2]  Michel Verleysen,et al.  Robust Bayesian clustering , 2007, Neural Networks.

[3]  H. Leung,et al.  Maximum Likelihood State Estimation of Semi-Markovian Switching System in Non-Gaussian Measurement Noise , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[4]  D. Bank.  A novel ultrasonic sensing system for autonomous mobile systems , 2002 .

[5]  W. Wu,et al.  Target racking with glint noise , 1993 .

[6]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[7]  Leonid I. Perlovsky,et al.  Cognitive high level information fusion , 2007, Inf. Sci..

[8]  Bijan Shirinzadeh,et al.  Random weighting estimation for fusion of multi-dimensional position data , 2010, Inf. Sci..

[9]  Christopher M. Bishop,et al.  Robust Bayesian Mixture Modelling , 2005, ESANN.

[10]  Richard J. Duro,et al.  On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development , 2010, Inf. Sci..

[11]  William D. Penny,et al.  Bayesian nonstationary autoregressive models for biomedical signal analysis , 2002, IEEE Transactions on Biomedical Engineering.

[12]  Sylvie Le Hégarat-Mascle,et al.  Fusion of Vegetation Indices Using Continuous Belief Functions and Cautious-Adaptive Combination Rule , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Robert L. Stevenson,et al.  Studentized Dynamical System for Robust Object Tracking , 2011, IEEE Transactions on Image Processing.

[14]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[15]  H. Leung,et al.  Chaotic radar signal processing over the sea , 1993 .

[16]  J. Tabrikian,et al.  MMSE-Based Filtering in Presence of Non-Gaussian System and Measurement Noise , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Matthew J. Beal.  Variational algorithms for approximate Bayesian inference , 2003 .

[18]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[19]  Fang Wang,et al.  The decision fusion in the wireless network with possible transmission errors , 2012, Inf. Sci..

[20]  Nikolas P. Galatsanos,et al.  Robust Image Segmentation with Mixtures of Student's t-Distributions , 2007, 2007 IEEE International Conference on Image Processing.

[21]  Ronald R. Yager,et al.  A framework for multi-source data fusion , 2004, Inf. Sci..

[22]  Y. Bar-Shalom,et al.  An interacting multiple model approach for target tracking with glint noise , 1994, Proceedings of 1994 IEEE National Radar Conference.

[23]  Yuan Gao,et al.  Sequential covariance intersection fusion Kalman filter , 2012, Inf. Sci..

[24]  G. Hewer,et al.  Robust Preprocessing for Kalman Filtering of Glint Noise , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[25]  Jeremy MG Taylor,et al.  Robust Statistical Modeling Using the t Distribution , 1989 .

[26]  Xuelong Li,et al.  Multi-Sensor Centralized Fusion without Measurement Noise Covariance by Variational Bayesian Approximation , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[27]  Sascha Spors,et al.  Joint audio-video object localization and tracking , 2001 .

[28]  Shian-Shyong Tseng,et al.  Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system , 2010, Inf. Sci..

[29]  R. Everson,et al.  Robust Autoregression: Student-t Innovations Using Variational Bayes , 2011, IEEE Transactions on Signal Processing.

[30]  Prabir Bhattacharya,et al.  A driver fatigue recognition model based on information fusion and dynamic Bayesian network , 2010, Inf. Sci..

[31]  WU WENG-RONG,et al.  Target Tracking With Glint Noise , 2010 .

[32]  Junbin Gao,et al.  Some remarks on Kalman filters for the multisensor fusion , 2002, Inf. Fusion.

[33]  Lorenzo Bruzzone,et al.  Image fusion techniques for remote sensing applications , 2002, Inf. Fusion.

[34]  Henry Leung,et al.  Covariance intersection based image fusion technique with application to pansharpening in remote sensing , 2010, Inf. Sci..

[35]  Magdi S. Mahmoud,et al.  State estimation with asynchronous multi-rate multi-smart sensors , 2012, Inf. Sci..