Modified RLS algorithm for unbiased estimation of FIR system with input and output noise

摘要

In the presence of both input and output noise, the classical least squares 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 modified recursive least squares (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.