Estimation of nonstationary AR model using the weighted recursive least square algorithm

A new method of estimating time-varying AR models using weighted recursive least square algorithm with a variable forgetting factor is described. The variable forgetting factor is adapted to a nonstationary signal by a generalized likelihood ratio algorithm through the so-called discrimination function which gives a good measure of nonstationarity. In this way we connect the results from the areas of nonstationary signal estimation and jump detection, and obtain an algorithm which exhibits a good tracking performance together with a high parameter estimation accuracy. The feasibility of the approach is demonstrated with both simulation data and real speech signals.