Sparsity Constrained Recursive Generalized Maximum Correntropy Criterion With Variable Center Algorithm.

This brief developed a novel sparsity constrained recursive adaptive filtering algorithm via the generalized correntropy criterion with variable center (GMCC-VC) for sparse system parameters estimation. In order to improve the performance of the adaptive filtering algorithm with GMCC under the non-Gaussian and non-zero mean noise environments, the GMCC is extended by adding a center which can be located at any position in the generalized Gaussian kernel function. Specifically, we proposed a novel adaptive online update method to optimize the kernel method and center. In addition, we mainly derived a recursive GMCC-VC algorithm based on the GMCC with the sparse constraint to exploit sparsity. Numerical simulation results are given to show that the proposed algorithm is efficient for sparse system parameter estimation problems in non-Gaussian and non-mean noise environments. [ABSTRACT FROM AUTHOR]