System Identification Using Reweighted Zero Attracting Least Absolute Deviation Algorithm

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.