Improved proportionate-type sparse adaptive filtering under maximum correntropy criterion in impulsive noise environments

Abstract

An improved proportionate adaptive filter based on maximum correntropy criterion (IP-MCC) is proposed for identifying systems with variable sparsity in an impulsive noise environment. Utilization of the MCC mitigates the effect of the impulsive noise while improved proportionate concept exploits the underlying system sparsity to improve the convergence rate. Performance analysis of the proposed IP-MCC reveals that the steady-state excess mean square error (EMSE) of the proposed IP-MCC filter is similar to that of MCC filter. Extensive simulations demonstrate that the proposed IP-MCC outperforms state-of-the-art algorithms in terms of convergence rate and detailed complexity analysis reveals that IP-MCC requires much less computational effort.