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<p>In this paper, an efficient and robust adaptive beamforming (RAB) method based on the general linear combination (GLC) is proposed to alleviate the performance degradation of traditional beamforming. Unlike methods based on the sample covariance matrix (SCM), the proposed method is applied to the covariance matrix (CM) improved by the GLC method, which avoids the residual intersect component between the desired component (DC) and interference component (IC). Subsequently, a block matrix is constructed based on conventional beamforming (CBF) in the interference region to suppress the DC for the interference-plus-noise covariance matrix (INCM) reconstruction. Furthermore, the main component is obtained via eigenvalue decomposition (EVD). Finally, the reconstructed INCM is acquired for RAB. The paper also includes a comparison of the performance of the proposed method with other RAB methods under various mismatches, demonstrating the effectiveness of the proposed method.</p>

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Robust Adaptive Beamforming via Generalized Linear Combination of Covariance Matrix with Eigenvalue Decomposition Refinement

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • Huichao Yang,
  • Jiayu Guo

摘要

In this paper, an efficient and robust adaptive beamforming (RAB) method based on the general linear combination (GLC) is proposed to alleviate the performance degradation of traditional beamforming. Unlike methods based on the sample covariance matrix (SCM), the proposed method is applied to the covariance matrix (CM) improved by the GLC method, which avoids the residual intersect component between the desired component (DC) and interference component (IC). Subsequently, a block matrix is constructed based on conventional beamforming (CBF) in the interference region to suppress the DC for the interference-plus-noise covariance matrix (INCM) reconstruction. Furthermore, the main component is obtained via eigenvalue decomposition (EVD). Finally, the reconstructed INCM is acquired for RAB. The paper also includes a comparison of the performance of the proposed method with other RAB methods under various mismatches, demonstrating the effectiveness of the proposed method.