TY - GEN
T1 - A zero-attracting quaternion-valued least mean square algorithm for sparse system identification
AU - Jiang, Mengdi
AU - Liu, Wei
AU - Li, Yi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10
Y1 - 2014/10
N2 - Recently, quaternion-valued signal processing has received more and more attention. In this paper, the quaternion-valued sparse system identification problem is studied for the first time and a zero-attracting quaternion-valued least mean square (LMS) algorithm is derived by considering the l1 norm of the quaternion-valued adaptive weight vector. By incorporating the sparsity information of the system into the update process, a faster convergence speed is achieved, as verified by simulation results.
AB - Recently, quaternion-valued signal processing has received more and more attention. In this paper, the quaternion-valued sparse system identification problem is studied for the first time and a zero-attracting quaternion-valued least mean square (LMS) algorithm is derived by considering the l1 norm of the quaternion-valued adaptive weight vector. By incorporating the sparsity information of the system into the update process, a faster convergence speed is achieved, as verified by simulation results.
KW - adaptive filtering
KW - LMS algorithm
KW - quaternion
KW - sparsity
KW - system identification
UR - http://www.scopus.com/inward/record.url?scp=84910650154&partnerID=8YFLogxK
U2 - 10.1109/CSNDSP.2014.6923898
DO - 10.1109/CSNDSP.2014.6923898
M3 - Conference article published in proceeding or book
AN - SCOPUS:84910650154
T3 - 2014 9th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2014
SP - 596
EP - 599
BT - 2014 9th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 9th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2014
Y2 - 23 July 2014 through 25 July 2014
ER -