System Identification Using Reweighted Zero Attracting Least Absolute Deviation Algorithm

TL;DR

This paper enhances system identification in non-Gaussian noise environments by integrating l1 norm penalties into LAD algorithms, demonstrating improved performance of ZA-LAD and RZA-LAD through simulations.

Contribution

It introduces a reweighted zero-attracting LAD algorithm with l1 penalty for better system identification under alpha-stable noise.

Findings

RZA-LAD outperforms LAD and ZA-LAD in non-Gaussian noise environments.

The proposed algorithms show improved accuracy in simulations.

Reweighted zero-attracting LAD effectively promotes sparsity in filter coefficients.

Abstract

In this paper, the l1 norm penalty on the filter coefficients is incorporated in the least mean absolute deviation (LAD) algorithm to improve the performance of the LAD algorithm. The performance of LAD, zero-attracting LAD (ZA-LAD) and reweighted zero-attracting LAD (RZA-LAD) are evaluated for linear time varying system identification under the non-Gaussian (alpha-stable) noise environments. Effectiveness of the ZA-LAD type algorithms is demonstrated through computer simulations.