Stable and efficient lattice methods for linear prediction

Abstract

A class of stable and efficient recursive lattice methods for linear prediction is presented. These methods guarantee the stability of the all-pole filter, with or without windowing of the signal, with finite wordlength computations, and at a computational cost comparable to the traditional autocorrelation and covariance methods. In addition, for data-compression purposes, quantization of the reflection coefficients can be accomplished within the recursion, if desired.