  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.
