Open AccessAdaptive filtering for jump Markov systems with unknown noise covariance
Author(s) -
Li Wenling,
Jia Yingmin
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0162
Subject(s) - inverse wishart distribution , wishart distribution , covariance , mathematics , filter (signal processing) , control theory (sociology) , noise (video) , covariance matrix , adaptive filter , computer science , algorithm , artificial intelligence , statistics , image (mathematics) , multivariate statistics , computer vision , control (management)
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.