TY - GEN
T1 - Regularized LAD algorithms for sparse time-varying system identification with outliers
AU - Wen, Fuxi
AU - Liu, Wei
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10
Y1 - 2016/10
N2 - Two regularized least mean absolute deviation (LAD) algorithms are proposed for sparse system identification, which are referred to as zero-Attracting LAD (ZA-LAD) and re-weighted zero-Attracting LAD (RZA-LAD), respectively. The LAD type algorithms are robust to the impulsive noises. Furthermore, ℓ1-norm penalty is imposed on the filter coefficients to exploit sparsity of the system. The performance of ZA-LAD type algorithms is evaluated for linear time varying system identification under impulsive noise environments through computer simulations.
AB - Two regularized least mean absolute deviation (LAD) algorithms are proposed for sparse system identification, which are referred to as zero-Attracting LAD (ZA-LAD) and re-weighted zero-Attracting LAD (RZA-LAD), respectively. The LAD type algorithms are robust to the impulsive noises. Furthermore, ℓ1-norm penalty is imposed on the filter coefficients to exploit sparsity of the system. The performance of ZA-LAD type algorithms is evaluated for linear time varying system identification under impulsive noise environments through computer simulations.
KW - least mean absolute deviation
KW - outliers
KW - sparsity
KW - time-varying system identification
KW - zero-Attracting
UR - https://www.scopus.com/pages/publications/85016227249
U2 - 10.1109/ICDSP.2016.7868630
DO - 10.1109/ICDSP.2016.7868630
M3 - Conference article published in proceeding or book
AN - SCOPUS:85016227249
T3 - International Conference on Digital Signal Processing, DSP
SP - 609
EP - 612
BT - Proceedings - 2016 IEEE International Conference on Digital Signal Processing, DSP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Digital Signal Processing, DSP 2016
Y2 - 16 October 2016 through 18 October 2016
ER -