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This work presents a cost-effective low-rank technique for designing robust adaptive beamforming (RAB) algorithms. The proposed technique is based on low-rank modelling of the mismatch and exploitation of the cross-correlation between the received data and the output of the beamformer. We construct a linear system of equations which computes the steering vector mismatch based on prior information...
In this work, we propose a low-complexity robust adaptive beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. LOCSME...
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