Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion

In this paper, we propose a novel simultaneous registration and fusion approach for tracking. This method is based on a recursive Variational Bayesian (RVB) algorithm, which is the online variant of the Variational Bayesian (VB) approach. Under the Bayesian framework, the states and parameters are recursively estimated. It is shown by simulation that the proposed RVB method has better estimation performance than the conventional approach.

[1]  Brendan J. Frey,et al.  A comparison of algorithms for inference and learning in probabilistic graphical models , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  D.G. Tzikas,et al.  The variational approximation for Bayesian inference , 2008, IEEE Signal Processing Magazine.

[3]  Naonori Ueda,et al.  Variational bayesian estimation and clustering for speech recognition , 2004, IEEE Transactions on Speech and Audio Processing.

[4]  Henry Leung,et al.  An exact maximum likelihood registration algorithm for data fusion , 1997, IEEE Trans. Signal Process..

[5]  Zenglin Xu,et al.  Bayesian Nonparametric Models for Multiway Data Analysis , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Henry Leung,et al.  State Estimation in Unknown Non-Gaussian Measurement Noise using Variational Bayesian Technique , 2013, IEEE Transactions on Aerospace and Electronic Systems.

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

[8]  R. Bishop,et al.  Solution to a multisensor tracking problem with sensor registration errors , 1999 .

[9]  Lawrence Carin,et al.  A Bayesian Nonparametric Approach to Image Super-Resolution , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[11]  Teng Joon Lim,et al.  The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM , 2007, IEEE Transactions on Signal Processing.

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

[13]  Jason L. Williams,et al.  An Efficient, Variational Approximation of the Best Fitting Multi-Bernoulli Filter , 2014, IEEE Transactions on Signal Processing.

[14]  Henry Leung,et al.  A Pseudo-Measurement Approach to Simultaneous Registration and Track Fusion , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Chong Wang,et al.  Nested Hierarchical Dirichlet Processes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  S. Challa,et al.  Joint sensor registration and track-to-track fusion for distributed trackers , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Teng Joon Lim,et al.  A Variational Inference Framework for Soft-In Soft-Out Detection in Multiple-Access Channels , 2009, IEEE Transactions on Information Theory.

[18]  William D. Penny,et al.  Variational Bayes for generalized autoregressive models , 2002, IEEE Trans. Signal Process..

[19]  H. Leung,et al.  Space-time registration of radar and ESM using unscented Kalman filter , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Václav Smídl,et al.  Variational Bayesian Filtering , 2008, IEEE Transactions on Signal Processing.

[21]  Henry Leung,et al.  An expectation-maximization-based interacting multiple model approach for cooperative driving systems , 2005, IEEE Transactions on Intelligent Transportation Systems.

[22]  Henry Leung,et al.  A variational Bayesian approach to robust sensor fusion based on Student-t distribution , 2013, Inf. Sci..

[23]  Xin Li,et al.  Patch-Based Video Processing: A Variational Bayesian Approach , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[24]  N. Nasios,et al.  Variational learning for Gaussian mixture models , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  Nikolas P. Galatsanos,et al.  Variational Bayesian Sparse Kernel-Based Blind Image Deconvolution With Student's-t Priors , 2009, IEEE Transactions on Image Processing.