Bias-compensated Adaptive Filter Algorithm under Minimum Error Entropy Criterion

Abstract

This paper proposes a bias-compensated adaptive filtering algorithm under minimum error entropy criterion, which outperforms with low steady-state misalignment for signal processing with noisy input in an environment containing impulsive output noise. In previous studies, much works use the minimum error entropy criterion, which is called MEE, to develop adaptive filter under the assumption of no input noise. However, pure input signal without any noise is nonexistent in the real-world environment. To address above issue, we introduce a bias-compensated vector into the traditional MEE algorithm and propose a bias-compensated adaptive filtering algorithm under minimum error entropy criterion named BCMEE, which has stronger robustness and higher convergence rate. The BCMEE utilizes a kernel function. In the kernel function, the slicing window size takes certain numbers of past errors during adaptation processing in BCMEE’s updating, whereas the other classical algorithm relies only on the current error signal and takes advantage of bias-compensated vector to compensate the bias of filter caused by the input noise. Simulation show the excellent performance of the proposed algorithms.