Modified LMS algorithm for unbiased impulse response estimation in nonstationary noise

In the presence of input interference, the Wiener solution for impulse response estimation is biased. It is proved that bias removal can be achieved by proper scaling of the optimal filter coefficients and a modified least mean squares (LMS) algorithm is then developed for accurate system identification in noise. Simulation results show that the proposed method outperforms two total least squares (TLS) based adaptive algorithms under nonstationary interference conditions