A New Variable Step-size Zero-point Attracting Projection Algorithm

TL;DR

This paper introduces a variable step-size scheme for the zero-point attracting projection algorithm, enhancing convergence speed and tracking ability in sparse filter estimation.

Contribution

It presents a novel VSS scheme based on sparseness gradient, improving ZAP algorithm performance over existing methods.

Findings

Faster convergence rate demonstrated in simulations.

Improved tracking ability shown in experiments.

Effective sparseness measure approximation used.

Abstract

This paper proposes a new variable step-size (VSS) scheme for the recently introduced zero-point attracting projection (ZAP) algorithm. The proposed variable step-size ZAPs are based on the gradient of the estimated filter coefficients sparseness that is approximated by the difference between the sparseness measure of current filter coefficients and an averaged sparseness measure. Simulation results demonstrate that the proposed approach provides both faster convergence rate and better tracking ability than previous ones.