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

TL;DR

This paper introduces variable-mixing parameter and quantized algorithms for kernel robust mixed-norm methods, improving impulsive noise handling and reducing computational complexity in nonlinear system identification.

Contribution

It proposes novel VPKRMN and QVPKRMN algorithms that adapt the mixing parameter and incorporate sparsification, addressing parameter selection and complexity issues in existing methods.

Findings

VPKRMN algorithms effectively adapt the mixing parameter.

QVPKRMN reduces computational complexity with sparsification.

Proposed algorithms outperform existing methods in impulsive noise scenarios.

Abstract

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.