摘要：Recently, a robust maximum total correntropy (MTC) adaptive filtering algorithm has been used in errors-in-variables (EIV) model in which both input and output data are contaminated with noises. As an extension of the maximum correntropy criterion (MCC), the MTC algorithm shows desirable performance in non-Gaussian noise environments. However, the MTC algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, a robust and sparse adaptive filtering algorithm, called zero attracting maximum total correntropy (ZA-MTC), is derived by adding al1norm penalty term to the maximum total correntropy algorithm in this brief. In addition, in the reweighted version, a log-sum function is employed to replace thel1norm penalty term. Simulation results demonstrate the advantages of the proposed algorithms under sparsity assumptions on the unknown parameter vector.
关键词：KeywordsMaximum total correntropySparse adaptive filteringZero attractingImpulsive noise suppressionNoisy input
