WANG Jie, YANG Guangquan, HU Yi, ZHANG Chunliang. A Two-Stage Nonlinear Shrinkage of the Sample Covariance Matrix for Robust Capon Beamforming[J]. Chinese Journal of Electronics, 2019, 28(5): 962-967. doi: 10.1049/cje.2019.06.016
Citation: WANG Jie, YANG Guangquan, HU Yi, ZHANG Chunliang. A Two-Stage Nonlinear Shrinkage of the Sample Covariance Matrix for Robust Capon Beamforming[J]. Chinese Journal of Electronics, 2019, 28(5): 962-967. doi: 10.1049/cje.2019.06.016

A Two-Stage Nonlinear Shrinkage of the Sample Covariance Matrix for Robust Capon Beamforming

doi: 10.1049/cje.2019.06.016
Funds:  This work is supported by the National Natural Science Foundation of China (No.11974086, No.51775122), the Special Innovation Project of Department of Education of Guangdong Province (No.2017KTSCX141), Guangzhou Science and Technology Project (No.201904010468), the Key Laboratory of Information Processing Transmission of Guangzhou (No.201605030014) and the Modern Video Audio Information Engineering Center of Guangdong Province.
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  • Corresponding author: ZHANG Chunliang (corresponding author) was born in 1964.He received the Ph.D.degree in mechanical engineering from Zhejiang University,China.He is a professor of School of Mechanical and Electric Engineering,Guangzhou University,China.His research interests include condition monitoring and control of complicated operating environment,robotics,signal analysis and processing,and intelligent algorithm research.(Email:zhangcl@gzhu.edu.cn)
  • Received Date: 2016-04-21
  • Rev Recd Date: 2018-07-25
  • Publish Date: 2019-09-10
  • When the number of snapshots used to estimate the Sample covariance matrix (SCM) approaches infinity and the array steering vector is accurately known, the Standard Capon beamformer (SCB) can better suppress spatial noises than data-independent beamformers. On the contrary, the performance of the SCB may decrease. To solve this problem, we propose a two-stage shrinkage scheme for the SCM. Specifically, in the first stage, the SCM is enhanced by the General linear combination (GLC) method, which will be referred to as GLC-SCM; and in the second stage, the GLCSCM is further improved with the Exponential matrix (EM) method, which will be referred to as GLC-EM-SCM. Compared with the conventional methods, the proposed method can achieve higher signal-to-interference-noise ratio output and more accurate signal power estimate.
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