Bias‐compensated affine‐projection‐like algorithms with noisy input. Issue 9 (1st April 2016) - Zhao, Haiquan; Zheng, Zongsheng
Abstract : A new class of bias‐compensated affine‐projection‐like (APL) algorithms is proposed, in which a bias‐compensation vector is derived to eliminate the bias caused by the noisy input. In addition, a new estimation method for the input noise variance is proposed which does not require the input–output noise variance ratio in advance. Simulations in a system identification context show that the proposed algorithms achieve significant improvements in steady‐state misalignment as compared with the conventional APL algorithms.