A new algorithm for robust adaptive beamforming

In practical array signal processing systems, traditional adaptive beamforming algorithms will degrade if some exploited assumptions become wrong or imprecise. In order to increase the robustness against the mismatch, robust beamforming brings about great improvement on array performances when compared with traditional methods. After introducing the traditional robust beamforming, it emphasizes the new robust beamforming which uses the Support Vector Machines (SVMs) to improve the generalization performance. By incorporating additional inequality constraints, this paper presents the modified SVM-based cost function and illustrates how it can be used to linear beamforming. In comparison with other robust beamforming techniques, simulation results show that the proposed SVM-based beamformer has the desired robust performance both in no-mismatch and mismatch scenarios.