ACCURATE ESTIMATION OF TRANSFER FUNCTION OF ALL-POLE MODEL SYSTEM FROM THE SIGNAL BURIED IN NOISE.

The authors describe a method for estimating accurately the parameters of an autoregressive (AR) process corrupted with high-level white noise. The method is based on phase matching by minimizing the difference between the phase of the all-zero model and that of the maximum-phase signal reconstructed from the power spectrum of the observed signal. The parameters of the AR model are obtained from the finite-length sequence of the estimated all-zero model. Since the phase matching technique is satisfied the conditions for in applying the least-mean-square method, AR parameters are estimated accurately even at the low signal-to-noise ratio.