A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

A sparsity-aware variable kernel width proportionate affine projection (AP) algorithm is devised for identifying sparse system in impulsive noise environments. For the devised algorithm, the symmetry maximum correntropy criterion (MCC) is employed to develop a new cost function for improving the PAP algorithm, then the variable kernel width and the l(p)-norm-like constraint are incorporated into the cost-function, which is named as l(p)-norm variable kernel width proportionate affine projection (LP-VPAP) algorithm. The devised LP-VPAP algorithm is investigated and verified under impulsive interference environments. Experimental results show that the LP-VPAP gets a faster convergence and provides a lower steady-state performance compared with AP, zero-attracting AP (ZA-AP), reweighted ZA-AP (RZA-AP), proportionate AP (PAP), MCC, variable kernel width MCC (VKW-MCC), and proportionate AP MCC (PAPMCC) algorithms.