Abstract:
An adaptive filter is derived in a Bayesian framework from the assumption that the difference in the parameter distribution from one time to another is bounded in terms o...Show MoreMetadata
Abstract:
An adaptive filter is derived in a Bayesian framework from the assumption that the difference in the parameter distribution from one time to another is bounded in terms of the Kullback-Leibler divergence. We show an explicit link to the general concepts of exponential forgetting, and outline the details for a linear Gaussian model with unknown parameter and covariance. We extend the problem to an unknown forgetting factor, where we provide a particular prior that allows for abrupt changes in forgetting, which is useful in change detection problems. The Rao-Blackwellized particle filter is used for the implementation, and its performance is assessed in a simulation of system with abrupt changes of parameters.
Published in: 2012 IEEE Statistical Signal Processing Workshop (SSP)
Date of Conference: 05-08 August 2012
Date Added to IEEE Xplore: 04 October 2012
ISBN Information:
Print ISSN: 2373-0803