RECURSIVE GENERALIZED MAXIMUM CORRENTROPY CRITERION ALGORITHM WITH SPARSE PENALTY CONSTRAINTS FOR SYSTEM IDENTIFICATION

To address the sparse system identification problem in a non-Gaussian impulsive noise environment, the recursive generalized maximum correntropy criterion (RGMCC) algorithm with sparse penalty constraints is proposed to combat impulsive-inducing instability. Specifically, a recursive algorithm based on the generalized correntropy with a forgetting factor of error is developed to improve the performance of the sparsity aware maximum correntropy criterion algorithms by achieving a robust steady-state error. Considering an unknown sparse system, the l(1)-norm and correntropy induced metric are employed in the RGMCC algorithm to exploit sparsity as well as to mitigate impulsive noise simultaneously. Numerical simulations are given to show that the proposed algorithm is robust while providing robust steady-state estimation performance.