Adaptive filtering for jump Markov systems with unknown noise covariance

作者：Li Wenling^*; Jia Yingmin

来源：IET Control Theory and Applications, 2013, 7(13): 1765-1772.

DOI：10.1049/iet-cta.2013.0162

• 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

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.

• 出版日期2013-9-5
• 单位北京航空航天大学