Kernel Affine Projection P-norm (KAPP) Filtering under Alpha Stable Distribution Noise Environment

Abstract

Aiming at improving the performance of the nonlinear adaptive filtering under the alpha-stable distribution noise environment, Kernel Affine Projection P-norm (KAPP) algorithm based on the minimum dispersion coefficient criterion and the affine projection is deduced. The accuracy of the gradient estimation is enhanced by using the input signals and the error signals at multiple times. The simulation results on Mackey–Glass chaotic time series prediction show that the KAPP algorithm has faster convergence speed, better steady-state performance and stronger robustness under the Gaussian noise and stable distributed noise environment.