Abstract
A robust adaptive beamformer (BF) with low computational complexity is proposed, where the adaptive weight is formulated as a linear combination of the training samples vectors and the target steering vector in the high interferences-to-noise ratio (INR) case. When the number of samples is greater than that of the interferences, an l1-norm constraint is imposed on the combinational vector to force its sparsity. Simulation results indicate that the proposed algorithm outperforms some classical robust BFs.
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