Haykin, SSayed, Ali H.Zeidler, JWei, PYee, P2017-12-192017-12-192017-12-19199710.1109/78.575687https://infoscience.epfl.ch/handle/20.500.14299/143391We exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered: one pertaining to a system identification problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applications, experiments are presented that demonstrate the tracking superiority of the extended RLS algorithms compared with the standard RLS and least-mean-squares (LMS) algorithms.text::journal::journal article::research article