AN INVERSE FACTORIZATION ALGORITHM FOR LINEAR PREDICTION

A new inverse factorization technique is presented for solving linear prediction problems arising in signal processing. The algorithm is similar to a scheme of Qiao in that it uses the rectangular Toeplitz structure of the data to recursively compute the prediction error and to solve the problem when the optimum filter order has been found. The novelty of the scheme presented here is the use of an inverse factorization scheme due to Pan and Plemmons for solving the linear prediction problem with low computational complexity and without the need for solving triangular systems. We also provide a linear systolic array for solving these problems.