A fast covariance type algorithm for sequential least-squares filtering and prediction

Abstract

Fast implementation of recursive least squares algorithms is of great importance in various estimation, control and signal processing applications. Such an efficient fast Kalman type algorithm is introduced in this paper for both the single channel and multichannel case without any windowing assumption (covariance case). Determination of the optimum parameters require 0(10p) block multiplications and additions per data point in contrast to existing schemes that apply only to single channel signals and call for 0(15p) multiplications and additions.