Atomic Norm Minimization-Based DoA Estimation for IRS-Assisted Sensing Systems

被引:0
|
作者
Li, Renwang [1 ,2 ]
Sun, Shu [1 ,2 ]
Tao, Meixia [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China
[2] Shanghai Jiao Tong Univ, Cooperat Medianet Innovat Ctr, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Direction-of-arrival estimation; Sensors; Estimation; Vectors; Covariance matrices; Multiple signal classification; Sparse matrices; Intelligent reflecting surface (IRS); direction-of-arrival (DoA) estimation; atomic norm minimization (ANM); Cram & eacute; r-Rao bound (CRB);
D O I
10.1109/LWC.2024.3437644
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Intelligent reflecting surface (IRS) is expected to play a pivotal role in future wireless sensing networks owing to its potential for high-resolution and high-accuracy sensing. In this letter, we investigate a multi-target direction-of-arrival (DoA) estimation problem in a semi-passive IRS-assisted sensing system, where IRS reflecting elements (REs) reflect signals from the base station to targets, and IRS sensing elements (SEs) estimate DoA based on echo signals reflected by the targets. First, instead of solely relying on IRS SEs for DoA estimation as done in the existing literature, this letter fully exploits the DoA information embedded in both IRS REs and SEs matrices via the atomic norm minimization (ANM) scheme. Subsequently, the Cram & eacute;r-Rao bound for DoA estimation is derived, revealing an inverse proportionality to MN3 + NM3 under the case of identity covariance matrix of the IRS measurement matrix and a single target, where M and N are the number of IRS SEs and REs, respectively. Finally, extensive numerical results substantiate the superior performance of the proposed method over representative baselines.
引用
收藏
页码:2672 / 2676
页数:5
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