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A recursive least squares (RLS) based fast parameter tracking algorithm with high noise immunity is proposed. The fast parameter tracking capability of the algorithm is achieved by perturbing the covariance matrix update equation whenever the signal model parameters change. Since the perturbing term depends on the auto- and crosscorrelations of the signal and algorithm outputs, the proposed algorithm is very robust with respect to noise. The efficiency of the algorithm has been verified by Monte-Carlo simulations.
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Jiang et al. (1992) studied this question.
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