Fast non-stationary lattice recursions for adaptive modeling and estimation

A class of least squares time-variant lattice recursions is presented, in which the lattice coefficients are sequences that are generated in a recursive test for matrix positivity. The computations of these sequences go partly along the lines of the test recursion and partly via recursive update procedures reflecting a low index of non Toeplitzness of the underlying matrix. In applications, these mixed form algorithms allow for a substantially reduction of the computational complexity, by performing stepwize tracking, in an exact manner, of estimation parameters.