Bias-Compensated Minimum Error Entropy Algorithms With Polynomial Sparsity Penalty Constraints

Abstract

This brief proposes a polynomial bias-compensated adaptive filtering algorithm for sparse system identification under minimum error entropy criterion, which outperforms in a sparse environment with noisy input and impulsive output noise. In previous studies, the minimum error entropy criterion (MEE) has been widely used in lots of works to develop adaptive filtering algorithms, which show better performance than the traditional algorithms under impulsive noise. However, the MEE algorithm not only has the essential assumption of no-noise input, but also loses the ability to identify the sparse system. To address above two issues, a bias-compensated vector is introduced into the previous traditional MEE algorithm and propose a bias-compensated adaptive filtering algorithm under minimum error entropy criterion named BCMEE algorithm, which compensates the bias caused by the input noise. By taking advantage of the capability of the sparse penalty term in sparse system identification, an attempt has been made to design a polynomial sparse BCMEE algorithm, named PZA-BCMEE algorithm. Simulation results prove the excellent performance of the proposed algorithms.