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2020, Volume 21, Issue 9

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Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 9 doi: 10.1631/FITEE.1900463

An improved subspace weighting method using random matrix theory

Affiliation(s): School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China; Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK; less

Received: 2019-09-02 Accepted: 2020-09-09 Available online: 2020-09-09
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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 (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 onto the real signal in the , 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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