Robust multichannel least mean square-type algorithms with fast decaying transient for blind identification of acoustic channels

The multichannel least mean square (MCLMS) is an attractive and effective algorithm for blind channel identification in the noise-free case. Some recent studies show that the performance of the MCLMS algorithm significantly deteriorates in a noisy environment, that is, the blind MCLMS solution does not remain collinear with the channel vector. Therefore the authors propose non-conventional technique that helps the MCLMS algorithm converge to a novel steady-state solution that is a weighted combination of all the eigenvectors, with the weight profile inversely proportional to the eigenvalues. The improved performance of the proposed solution is verified both analytically and numerically. The algorithm is then optimised by introducing an adaptive step size that ensures fast decay of the transient response, giving stability as well as rapid convergence to the final solution. The authors then apply the proposed technique to different variants of the MCLMS algorithm, including frequency-domain implementations, to achieve a noise-robust performance. Computer simulations are presented that show improved performance of the proposed algorithms for blind identification of both acoustic and random channels with noise.