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Robust adaptive b eamforming base d on virtual sensors using low-complexity spatial sampling
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DOI：10.1016/j.sigpro.2021.108172.png)
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Abstract
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The performance of robust adaptive beamforming (RAB) based on interference-plus-noise covariance (IPNC) matrix reconstruction can be degraded seriously in the presence of random mismatches (look direction and array geometry), particularly when the input signal-to-noise ratio (SNR) is high. In this work, we present a RAB technique to address covariance matrix reconstruction problems. The proposed RAB technique involves IPNC matrix reconstruction using a low-complexity spatial sampling process (LCSSP) and employs a virtual received array vector. In particular, the power spectrum sampling is realized by a proposed projection matrix in a higher dimension. The essence of the proposed technique is to avoid reconstruction of the IPNC matrix by integrating over the angular sector of the interference-plus-noise region. Simulation results are presented to verify the effectiveness of the proposed RAB approach. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Covariance matrix reconstruction
Robust adaptive beamforming
Spatial spectrum process
Virtual sensors
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