AR spectral estimation based on multi-window analysis of the linear prediction error

Abstract

A new method for autoregressive (AR) spectral analysis and a fast-transversal-filters (FTF) recursive algorithm are introduced. While conventional least-squares (LS) methods use a single windowing function in the analysis of the linear prediction error, the proposed method decomposes the linear prediction error into several bands and analyzes each of them through a different window. With this approach, the variance of spectral estimates and the tracking ability of the spectral analyzer can be traded off throughout the frequency spectrum, giving rise to spectral estimates that represent the true underlying spectrum with better fidelity than conventional LS methods. Mathematical background for the design of fast recursive algorithms for multi-window LS is exposed and an FTF algorithm is derived. Simulations comparing the performance of conventional and multi-window LS are shown.