Abstract
This paper compares the tracking performance of state space recursive least squares (SSRLS) and state space least mean square (SSLMS) algorithms for a chirped signal buried in additive white Gaussian noise. The signal is a sinusoid whose frequency is drifting at a constant rate. After incorporating second order linear time varying state space model of the chirped sinusoid into their formulation, both SSRLS and SSLMS exhibit superior tracking performance over standard RLS & LMS and their known variants. The performance comparison is based on the evaluation of time average auto-correlation function (ACF) of prediction errors of SSRLS and SSLMS when responding to the chirped signal for different values of forgetting factor (SSRLS) & step-size parameter (SSLMS). Relative whiteness of prediction errors of SSRLS and SSLMS gives a measure for comparing their tracking performance. Tracking results for standard RLS and LMS are also reported
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