Adaptive parameter estimation of autoregressive signals from noisy observations

This paper presents a new type of improved least-squares (ILS) algorithm for adaptive parameter estimation of autoregressive (AR) signals from noisy observations. Unlike the previous ILS-based methods, the developed algorithm can give consistent parameter estimates in a very direct manner that does not involve dealing with an augmented noisy AR model. The new algorithm is demonstrated to outperform the previous ILS-based methods in terms of its improved numerical efficiency.