An Estimation Algorithm For Ar Models With Closely Located Lightly Damped Low Frequency Poles

In this paper we present a pole estimation algorithm which is based on an overdetermined adaptive IIR filter with an additional postprocessing stage to extract the pole locations from the adaptive weights. The adaptive filtering algorithm used, is a pseudo-linear regression algorithm which is solved by a time-recursive QR decomposition. Two pole classification schemes are presented to separate the true poles and the superfluous poles. The classification schemes are based on the occurrence of pole-zero cancelation and on the pole movement in the z-plane. Floating point simulations are presented to demonstrate the performance of the proposed algorithm. 1. INTRODUCTION Autoregressive (AR) modeling [1] is an important issue in many signal processing and control applications. Some examples are: speech analysis, where the poles of the AR model determine the formant frequencies and bandwidths which can then be used further for coding and voice systems; sensor array processing, where the dire..