Adaptive filtering for jump Markov systems with unknown noise covariance. Issue 13 (1st September 2013)

• Record Type: Journal Article
• Authors: Li, Wenling; Jia, Yingmin
• Is Part Of: IET control theory & applications. Volume 7:Issue 13(2013)
• Page Start: 1765
• Page End: 1772
• Publication Date: 2013-09-01
• Subjects: adaptive filters -- approximation theory -- Bayes methods -- covariance matrices -- estimation theory -- Markov processes -- random processes; mode‐likelihood function -- mode‐conditioned estimate -- variational Bayesian approximation method -- inverse‐Wishart distribution -- random matrix -- unknown measurement noise covariance -- adaptive filtering -- jump Markov systems; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2013.0162
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : The paper proposed an adaptive filter for jump Markov systems with unknown measurement noise covariance. The filter is derived by treating covariance as a random matrix and an inverse‐Wishart distribution is adopted as the conjugate prior. The variational Bayesian approximation method is employed to derive mode‐conditioned estimates and mode‐likelihood functions in the framework of interacting multiple model. A numerical example is provided to illustrate the performance of the proposed filter.