Adaptive Convex Combination of APA and ZA-APA algorithms for Sparse System Identification

TL;DR

This paper introduces an adaptive convex combination of APA and ZA-APA algorithms that dynamically adjusts to system sparsity, enhancing identification performance across sparse and non-sparse systems while maintaining robustness against colored inputs.

Contribution

It proposes a novel adaptive convex combination method that switches between APA and ZA-APA based on system sparsity, improving system identification accuracy.

Findings

Converges to APA for non-sparse systems.

Achieves lower steady-state EMSE for semi-sparse systems.

May outperform individual filters in highly sparse environments.

Abstract

In general, one often encounters the systems that have sparse impulse response, with time varying system sparsity. Conventional adaptive filters which perform well for identification of non-sparse systems fail to exploit the system sparsity for improving the performance as the sparsity level increases. This paper presents a new approach that uses an adaptive convex combination of Affine Projection Algorithm (APA) and Zero-attracting Affine Projection Algorithm (ZA-APA)algorithms for identifying the sparse systems, which adapts dynamically to the sparsity of the system. Thus works well in both sparse and non-sparse environments and also the usage of affine projection makes it robust against colored input. It is shown that, for non-sparse systems, the proposed combination always converges to the APA algorithm, while for semi-sparse systems, it converges to a solution that produces lesser steady state EMSE than produced by either of the component filters. For highly sparse systems, depending on the value of the proportionality constant ($\rho$) in ZA-APA algorithm, the proposed combined filter may either converge to the ZA-APA based filter or produce a solution similar to the semi-sparse case i.e., outerperforms both the constituent filters.