Variable-mixing parameter quantized kernel robust mixed-norm algorithms for combating impulsive interference
For researchers working on nonlinear system identification under impulsive noise, this work offers improved algorithms that address parameter tuning and computational efficiency, though it is incremental.
The paper proposes variable-mixing parameter KRMN algorithms to solve the parameter selection problem and a quantized version to reduce computational complexity. Simulations show superior performance over existing algorithms in nonlinear system identification under 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 the KRMN algorithm grows linearly as the iteration goes on, thus it has high computational complexity and memory requirements. 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 with impulsive interferences. The energy conservation relation (ECR) and convergence property of the QVPKRMN algorithm are analyzed. Simulation results in the context of nonlinear system identification under impulsive interference demonstrate the superior performance of the proposed VPKRMN and QVPKRMN algorithms as compared with the existing algorithms.