Robust estimation of autoregressive processes using a mixture-based filter-bank

A mixture-based framework for robust estimation of ARX-type processes is presented. The ARX process is presumed to suffer from an unknown noise and/or distortion. The approach taken here is to model the overall degraded process via a mixture. Each component of this mixture uses the same ARX model but explores a different noise/distortion process. Estimation of this mixture unifies the preprocessing and process modelling tasks. The quasi-Bayes (QB) procedure for mixture identification is extended to yield a fast recursive update of the estimator statistics. This allows non-stationary noise/distortion effects to be tracked. An application in on-line outlier-robust estimation of an AR process is given.