Multichannel autoregressive spectral estimation from noisy observations

We present a new method for multichannel autoregressive power spectrum estimation from a finite set of noisy observations without a priori knowledge of additive noise power. The method is based on the Yule-Walker equations and estimates the autoregressive parameters from a finite set of measured data and then the power spectrum. An inverse filtering technique is used to estimate the AR parameters and the observation noise variances, simultaneously. The procedure is iterative. Computer simulation results that demonstrate the performance of the estimation method are presented