A Shrinkage L1-Norm Constrained LMS Algorithm for Adaptive Sparse Array Beamforming

被引：0
|
作者：
Shi, Wanlu [1 ]
Li, Yingsong [1 ,2 ]
机构：
[1] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin, Peoples R China
[2] Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Microwave Remote Sensing, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 2019 9TH IEEE-APS TOPICAL CONFERENCE ON ANTENNAS AND PROPAGATION IN WIRELESS COMMUNICATIONS (IEEE APWC' 19) | 2019年
基金：
中央高校基本科研业务费专项资金资助;
关键词：
adaptive beamforming algorithm; least-mean-square; normalized lenst-mean-square; sparse array beamforming; noise-free method; CHANNEL ESTIMATION; NORM;
D O I：
暂无
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
A constrained least-mean-square (CLMS) algorithm using shrinkage method and l(1)-norm penalty, namely, the shrinkage l(1)-norm penalized CLMS (SL1-CLMS) algorithm is proposed for adaptive beamforming. The introduced algorithm can successfully exploit the sparse characteristics of the antenna array utilized in beamformer. Besides, the new algorithm maintains the desired performance, i.e., greeting main lobe and nulls corresponding to the signal of interest (SOI) and interferences. The SL1-CLMS algorithm employs an alterable step step which is acquired through the l(1)-l(2) minimization method to accelerate the convergence rate. Simulation results attest the validity of the SL1-CLMS algorithm.
引用
收藏
页码：281 / 284
页数：4
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