Adaptive estimation of time-varying parameters in AR models with variable forgetting factor

A new method for estimating time-varying parameters of nonstationary AR signal models, based on adaptive recursive least squares with variable forgetting factors, is described. The adaptive estimator differs from the conventional one by the simultaneously estimation of AR model parameters and scale factor of prediction residuals, while the variable forgetting factor values are adapted to the nonstationary signal via a new extended prediction error detection scheme. The method has good adaptability in the non-stationary situations, and gives low bias and low variance at the stationary situations. The feasibility of the approach is demonstrated with simulations.