An Improved Variable Step-size Zero-point Attracting Projection Algorithm

TL;DR

This paper introduces an enhanced VSS scheme for the ZAP algorithm that adapts based on sparseness difference, improving convergence speed and tracking in system identification.

Contribution

It presents a novel VSS scheme for ZAP that works for both sparse and non-sparse systems, improving performance over previous methods.

Findings

Faster convergence rate compared to previous algorithms

Better tracking ability demonstrated in simulations

Effective for both sparse and non-sparse systems

Abstract

This paper proposes an improved variable step-size (VSS) scheme for zero-point attracting projection (ZAP) algorithm. The proposed VSS is proportional to the sparseness difference between filter coefficients and the true impulse response. Meanwhile, it works for both sparse and non-sparse system identification, and simulation results demonstrate that the proposed algorithm could provide both faster convergence rate and better tracking ability than previous ones.