A bias‐compensated proportionate NLMS algorithm with noisy input signals. (9th September 2019) - Yoo, JinWoo; Shin, JaeWook; Park, PooGyeon
Summary: This paper proposes a novel proportionate normalized least‐mean‐squares (PNLMS) algorithm that is robust to input noises. Through compensating for biases due to input noise added at the filter input, the proposed PNLMS algorithm avoids performance deterioration owing to the noisy input signals. Moreover, since the proposed PNLMS algorithm uses a new gain‐distribution matrix, it has a fast convergence rate compared with the existing PNLMS algorithms, even when there is no input noise. The experimental results verify that the proposed PNLMS algorithm enhances the filter performance for sparse system identification in the presence of input noises. Abstract : This paper proposes a novel proportionate normalized least‐mean‐squares (PNLMS) algorithm with the robustness against noisy input signals. Through compensating for a bias occurred by the input noise that is added at the filter input, the proposed PNLMS algorithm avoids the performance degradation caused by the noisy input signals. Because the proposed PNLMS algorithm uses a new gain‐distribution matrix, it has a fast convergence rate as compared with the existing PNLMS algorithms, even when the input noise does not exist.