Variable-mixing parameter quantized kernel robust mixed-norm algorithms for combating impulsive interference

Although the kernel robust mixed-norm (KRMN) algorithm outperforms the kernel least mean square (KLMS) algorithm in impulsive noise, it still has two major problems as follows: (1) The choice of the mixing parameter in the KRMN is crucial to obtain satisfactory performance. (2) The structure of KRMN grows linearly as the iteration goes on, thus it has high computational burden and memory requirement. To solve the parameter selection problem, two variable-mixing parameter KRMN (VPKRMN) algorithms are developed in this paper. Moreover, a sparsification algorithm, quantized VPKRMN (QVPKRMN) algorithm is introduced for nonlinear system identification under impulsive interference environment. The energy conservation relation (ECR) and convergence property of QVPKRMN algorithm is analyzed. Simulations in the context of nonlinear system identification under impulsive interference demonstrate that the proposed VPKRMN and QVPKRMN algorithms provide superior performance than existing algorithms.