A sparsity-aware proportionate normalized maximum correntropy criterion algorithm for sparse system identification in non-Gaussian environment

A sparsity-aware proportionate normalized maximum correntropy criterion (PNMCC) algorithm with lp-norm penalty, which is named as lp-norm constraint PNMCC (LP-PNMCC), is proposed and its crucial parameters, convergence speed rate and steady-state performance are discussed via estimating a typical sparse multipath channel and an typical echo channel. The LP-PNMCC algorithm is realized by integrating a lp-norm into the PNMCC's cost function to create an expected zero attraction term in the iterations of the presented LP-PNMCC algorithm, which aims to further exploit the sparsity property of the sparse channels. The presented LP-PNMCC algorithm has been derived and analyzed in detail. Experimental results obtained from sparse channel estimations demonstrate that the proposed LP-PNMCC algorithm is superior to the PNMCC, PNLMS, RZA-MCC, ZA-MCC, NMCC and MCC algorithms according to the convergence speed rate and steady-state mean square deviation.