Accurate noise compensation technique for the identification of multichannel AR processes with noise

This paper presents a new method for the identification of a time-invariant multichannel autoregressive (AR) process corrupted by additive white observation noise. The method is based on the Yule-Walker equations and identifies the AR parameters from a finite set of a measured data only. The input signals to the underlying process are assumed to be unknown. Inverse filtering technique is used to estimate the AR parameters as well as the observation noise variance simultaneously. The procedure is iterative. Computer simulation results that illustrate the performance of the identification method are provided