Identification of time-varying linear channels

Abstract

We present a new method for on-line identification of time-varying FIR channels. Two conditionally coupled estimators are proposed. In both cases an augmented-state adaptive Kalman filter is employed for tracking the time-varying channel and estimating the mean channel response. Coupled to the Kalman filter is an algorithm for estimating the parameters of the underlying auto-regressive (AR) model which describes the time evolution of the channel. For the first coupled estimator, we propose a new recursive least squares algorithm for estimation of these AR parameters directly from the channel observations. An alternative algorithm based on estimation of the channel covariance is used in the second coupled estimator. A simulation example demonstrates the performance of the proposed estimators.