Full Text:   <330>

Summary:  <47>

CLC number: TP319

On-line Access: 2020-09-09

Received: 2019-09-02

Revision Accepted: 2019-12-30

Crosschecked: 2020-08-10

Cited: 0

Clicked: 692

Citations:  Bibtex RefMan EndNote GB/T7714


Yu-meng Gao


Ye-chao Bai


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.9 P.1302-1307


An improved subspace weighting method using random matrix theory

Author(s):  Yu-meng Gao, Jiang-hui Li, Ye-chao Bai, Qiong Wang, Xing-gan Zhang

Affiliation(s):  School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China; more

Corresponding email(s):   ychbai@nju.edu.cn

Key Words:  Direction of arrival, Signal subspace, Random matrix theory

Yu-meng Gao, Jiang-hui Li, Ye-chao Bai, Qiong Wang, Xing-gan Zhang. An improved subspace weighting method using random matrix theory[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1302-1307.

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author="Yu-meng Gao, Jiang-hui Li, Ye-chao Bai, Qiong Wang, Xing-gan Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T An improved subspace weighting method using random matrix theory
%A Yu-meng Gao
%A Jiang-hui Li
%A Ye-chao Bai
%A Qiong Wang
%A Xing-gan Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 21
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900463

T1 - An improved subspace weighting method using random matrix theory
A1 - Yu-meng Gao
A1 - Jiang-hui Li
A1 - Ye-chao Bai
A1 - Qiong Wang
A1 - Xing-gan Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 9
SP - 1302
EP - 1307
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900463

The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (MUSIC) algorithm in the case of low signal-to-noise ratio (SNR) and when signals are correlated. In this study, we use the random matrix theory (RMT) to improve WSF. RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate. The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance. Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory, the method of calculating WSF is obtained. Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.





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