Abstract
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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Project supported by the National Natural Science Foundation of China (No. 61976113)
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Ye-chao BAI designed the research. Yu-meng GAO and Ye-chao BAI processed the data. Yu-meng GAO drafted the manuscript. Ye-chao BAI, Jiang-hui LI, and Qiong WANG helped organize the manuscript. Yu-meng GAO, Qiong WANG, and Xing-gan ZHANG revised and finalized the paper.
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Yu-meng GAO, Jiang-hui LI, Ye-chao BAI, Qiong WANG, and Xing-gan ZHANG declare that they have no conflict of interest.
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Gao, Ym., Li, Jh., Bai, Yc. et al. An improved subspace weighting method using random matrix theory. Front Inform Technol Electron Eng 21, 1302–1307 (2020). https://doi.org/10.1631/FITEE.1900463
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DOI: https://doi.org/10.1631/FITEE.1900463