Multiple Measurement Vector Sparsity Adaptive Algorithm Realize the non-orthogonal Waveform Separation of MIMO Sparse Linear Array

被引：0

作者：

Chen, Qiao ^{[1
]}

Tong, Ningning ^{[1
]}

Dong, Yibei ^{[2
]}

Bao, Lei ^{[1
]}

Chen, Shuai ^{[1
]}

Han, Lixun ^{[1
]}

机构：

[1] Air Force Engn Univ, Air & Missile Def Coll, Xian, Peoples R China

[2] Air Force Engn Univ, Minist Basic, Xian, Peoples R China

来源：

PROCEEDINGS OF 2019 IEEE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION AND COMMUNICATION TECHNOLOGY (ICEICT 2019) | 2019年

关键词：

IRCM; MMV; waveform separation; sparse degree adaptive; compression sensing; IMAGING METHOD; OUTPUT RADAR; APPROXIMATION;

D O I：

10.1109/iceict.2019.8846423

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In order to solve the irregular crossing distance migration (IRCM) in the compression sensing waveform separation method, the paper studies the characteristics of the signal received by MIMO radar and explores the joint sparsity of the multiple measurement vector in different observation channels. Multiple measurement vector sparse recovery algorithm is used to separate the echo replaced the single-observation vector restoration algorithm. It cannot only reconstruct the one-dimensional distance image with high precision but also suppress the IRCM. Aiming at the problem that current joint sparse recovery algorithm has poor separation effect due to the unknown sparsity, a sparse degree adaptive joint sparse recovery algorithm is proposed to improve the effect of waveform separation.

引用

页码：226 / 230

页数：5