In the presence of both input and output noise, the classical leastsquares solution for finite-impulse response (FIR) estimation is biased. It has been shown that bias can be removed by properly scaling the optimal F...
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In the presence of both input and output noise, the classical leastsquares solution for finite-impulse response (FIR) estimation is biased. It has been shown that bias can be removed by properly scaling the optimal FIR filter coefficients in the least-squares (LS) criterion. A modifiedrecursiveleastsquares (MRLS) algorithm is proposed for accurate identification of a system with both input and output noise. Simulation results show that this method outperforms the modified LMS algorithm under non-stationary interference conditions.
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