Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Relationships between adaptive minimum variance beamforming andoptimal source localization
IEEE Transactions on Signal Processing
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Decoupled estimation of DOA and angular spread for a spatiallydistributed source
IEEE Transactions on Signal Processing
On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Maximum likelihood separation of constant modulus signals
IEEE Transactions on Signal Processing
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
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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.