Tracking of linear time-variant systems

In this paper we 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 are considered, one pertaining to a system identification problem and the other to the tracking of a chirped sinusoid in additive noise. For both applications, experiments are presented that demonstrate the tracking optimality of the extended RLS algorithms, compared with the standard RLS and least-mean-squares (LMS) algorithms.

Published in:
Conference Record on the IEEE Military Communications Conference, 1995. MILCOM'95,, 2, 602-606
Presented at:
IEEE Military Communications Conference - MILCOM, San Diego, CA, USA, November 5-8, 1995

 Record created 2017-12-19, last modified 2018-03-17

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