Adaptive Tracking of Linear Time - Variant Systems
نویسندگان
چکیده
| 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 of the extended RLS algorithm are considered, one pertaining to a system identiication 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.
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