Robust adaptive beamformers based on worst-case optimization and constraints on magnitude response
IEEE Transactions on Signal Processing
Consistent reduced-rank LMMSE estimation with a limited number of samples per observation dimension
IEEE Transactions on Signal Processing
Robust adaptive beamforming in partly calibrated sparse sensor arrays
IEEE Transactions on Signal Processing
Super-Gaussian loading for robust beamforming
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Broadband MVDR beamformer applying PSO
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Detection of heterogeneous samples based on loaded generalized inner product method
Digital Signal Processing
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Minimum variance beamformers are usually complemented with diagonal loading techniques in order to provide robustness against several impairments such as imprecise knowledge of the steering vector or finite sample size effects. This paper concentrates on this last application of diagonal loading techniques, i.e., it is assumed that the steering vector is perfectly known and that diagonal loading is used to alleviate the finite sample size impairments. The analysis herein is asymptotic in the sense that it is assumed that both the number of antennas and the number of samples are high but have the same order of magnitude. Borrowing some results of random matrix theory, the authors first derive a deterministic expression that describes the asymptotic signal-to-noise-plus-interference ratio (SINR) at the output of the diagonally loaded beamformer. Then, making use of the statistical theory of large observations (also known as general statistical analysis or G-analysis), the authors derive an estimator of the optimum loading factor that is consistent when both the number of antennas and the sample size increase without bound at the same rate. Because of that, the estimator has an excellent performance even in situations where the quotient between the number of observations is low relative to the number of elements of the array.