Martin Morf

TL;DR: A discussion of some of the most interesting recent developments in the area of real time (or "on-line") algorithm for estimation and parameter tracking using ladder canonical forms for AR and ARMA modeling is presented.

TL;DR: In this paper, it was shown that the matrix covariance function of a multivariate stationary process can be characterized by a sequence of matrix partial correlations, having singular values less than or equal to one in magnitude.

TL;DR: New examples of the new exact least-squares recursions for ladder forms, such as Covariance Ladder Form that is equivalent to the so-called Covariances Method or Recursive Least Squares are presented.

TL;DR: In this paper, the extension of the all-pole (AR) exact least-squares ladder algorithms to the pole-zero (ARMA) case is presented, based on a general set of recursions obtained by a geometric approach.

TL;DR: This work has developped fast algorithms for a variety of presently available identification methods as well as new ones that require computer time and storage per measurement only proportional to the number of model parameters, compared to the square of theNumber of parameters for previous methods.