Journal
SIGNAL PROCESSING
Volume 96, Issue -, Pages 375-381Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.sigpro.2013.10.009
Keywords
Compressive sensing; DOA estimation; Robust adaptive beamforming; Sparse reconstruction
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Funding
- National Natural Science Foundation of China [61102134]
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Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in the training data. In this paper, we reconstruct the interference-plus-noise covariance matrix in a sparse way, instead of searching for an optimal diagonal loading factor for the sample covariance matrix. Using sparsity, the interference covariance matrix can be reconstructed as a weighted sum of the outer products of the interference steering vectors, the coefficients of which can be estimated from a compressive sensing (CS) problem. In contrast to previous works, the proposed CS problem can be effectively solved by use of a priori information instead of using l(1)-norm relaxation or other approximation algorithms. Simulation results demonstrate that the performance of the proposed adaptive beamformer is almost always equal to the optimal value. (C) 2013 Elsevier B.V. All rights reserved.
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