ADAPTIVE TRACKING OF LINEAR TIME-VARIANT SYSTEMS BY EXTENDED RLS ALGORITHMS

In this paper, we exploit the one-to-one correspondences between the r ecursive least-squares (RLS) and Kalman variables to formulate extende d forms of the RLS algorithm, Two particular forms of the extended RLS algorithm are considered: one pertaining to a system identification p roblem and the other pertaining to the tracking of a chirped sinusoid in additive noise, For both of these applications, experiments are pre sented that demonstrate the tracking superiority of the extended RLS a lgorithms compared with the standard RLS and least-mean-squares (LMS) algorithms.