Adaptive recovery of a noisy chirp: performance of the SSLMS algorithm (original) (raw)

Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005.

This paper investigates the ability of state space least mean square (SSLMS) algorithm to track 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, SSLMS exhibits superior tracking performance over standard LMS & RLS and their known variants. The step size parameter plays an important role in this context. For various values of step size parameter, time average auto-correlation function (ACF) of prediction error is evaluated when responding to chirped signal. Whiteness of prediction error verifies excellent tracking by SSLMS.

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