Real-Valued DOA Estimation Utilizing Enhanced Covariance Matrix With Unknown Mutual Coupling

被引:10
|
作者
Tian, Ye [1 ]
Wang, Ran [2 ]
Chen, Hua [1 ]
Qin, Yunbai [3 ]
Jin, Ming [1 ]
机构
[1] Ningbo Univ, Fac Informat Sci & Engn, Ningbo 315211, Peoples R China
[2] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China
[3] Guangxi Normal Univ, Sch Elect Engn, Guilin 541004, Peoples R China
基金
中国国家自然科学基金;
关键词
Estimation; Covariance matrices; Direction-of-arrival estimation; Mutual coupling; Massive MIMO; Eigenvalues and eigenfunctions; Convolutional neural networks; Direction of arrival (DOA) estimation; enhanced covariance estimation; unknown mutual coupling; RBLW estimator; real-valued transformation; SPARSE REPRESENTATION; ARRAY;
D O I
10.1109/LCOMM.2022.3148260
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Due to the space limitations, the array in a massive multiple-input multiple-output (MIMO) system often suffers from unknown mutual coupling. Meanwhile, small records of data observations may coexist. Such two limitations bring a challenge for accurate direction-of-arrival (DOA) estimation. To conquer this challenge, a real-valued DOA estimation method is proposed in this letter, whose core is to eliminate the influence of unknown mutual coupling by the inherent mechanism, as well as enhance the sampled covariance matrix estimation with the linear shrinkage technique combined with Rao-Blackwell Ledoit-Wolf (RBLW) estimator under the case of small sample size. Considering the result that the direct usage of the shrinkage target of RBLW estimator can yield an improved DOA estimation under low SNRs, a modified method depends on the eigenvalue comparison is also addressed. Simulation results show that the proposed method can provided an increased accuracy with reduced complexity.
引用
收藏
页码:912 / 916
页数:5
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