| Citation: | CAO Silei, ZENG Weigui, WANG Leiet al. A robust beamforming algorithm based on double-layer reconstruction of covariance matrix[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1605-1611. doi: 10.13700/j.bh.1001-5965.2020.0297(in Chinese)
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Focusing on the problem that the performance of traditional adaptive beamformer declines sharply when the covariance matrix contains the target signal component and the target steering vector mismatch, a robust beamforming algorithm based on double-layer reconstruction of interference-plus-noise covariance matrix is proposed in this paper. Firstly, sparse reconstruction method is used to estimate interference-plus-noise covariance matrix. The interference-plus-noise covariance matrix is optimized by estimating the interference steering vector and interference power. Secondly, based on subspace theory, an optimization model of steering vector constraint error is established, and the convex optimization model is solved by iterative method to obtain the optimal weight vector. The simulation results show that the proposed algorithm improves the robustness of the beamformer in the case of target vector constraint error and array error. The algorithm performs well in low-speed snapshot and its output performance is better than the existing methods.
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