The nature of statistical learning theory
The nature of statistical learning theory
Digital Beamforming in Wireless Communications
Digital Beamforming in Wireless Communications
Robust Adaptive Beamforming under Uncertainty in Source Direction-of-Arrival
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
Convergence of the IRWLS Procedure to the Support Vector Machine Solution
Neural Computation
Robust Array Beamforming With Sidelobe Control Using Support Vector Machines
IEEE Transactions on Signal Processing
Theory and application of covariance matrix tapers for robustadaptive beamforming
IEEE Transactions on Signal Processing
Bayesian Beamforming for DOA Uncertainty: Theory and Implementation
IEEE Transactions on Signal Processing
A Bayesian approach to robust adaptive beamforming
IEEE Transactions on Signal Processing
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The conventional Bayesian beamformer suffers substantial performance degradation, when the true direction-of-arrival is deterministic and is not included in the priori. In this letter, we propose a method with sidelobe constraint to improve the robustness of the Bayesian beamforming method. Support vector machine is used to obtain the weights. Numerical results show that the proposed beamformer can improve the Bayesian beamforming performance, and can output a relatively higher signal-to-noise-plus-interference ratio even when the desired direction-of-arrival is not included in the Bayesian priori region.