title: Second-order cone programming with probabilistic regularization for robust adaptive beamforming.
abstract: Probabilistic regularization (PR) is introduced to make superdirective array beamforming robust against sensor characteristic mismatches. The objective is to enlarge the directivity while ensuring robustness with high probability. The PR problem is solved via the second-order cone programming where the regularization parameter is chosen through a statistical analysis of the system perturbations, based on Monte Carlo simulations. Experiments are carried out on a miniaturized 3 × 3 uniform rectangular array without calibration. The results show that for this particular array, the PR method is robust to sensor mismatches and achieves a higher level of directivity compared with other robust adaptive beamforming approaches.
authors.affiliation: Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China xguo@nwpu.edu.cn.
authors.forename: Xijing
authors.initials: X
authors.lastname: Guo
authors.affiliation: Université de Lorraine, Centre de Recherche en Automatique de Nancy, Unité Mixte de Recherche 7039, Vandœuvre-lès-Nancy, F-54506, France sebastian.miron@univ-lorraine.fr.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Miron
authors.affiliation: Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China yxyang@nwpu.edu.cn.
authors.forename: Yixin
authors.initials: Y
authors.lastname: Yang
authors.affiliation: College of Underwater Acoustic Engineering, Harbin Engineering University, 145 Nantong Street, 150001 Harbin, China yangshie@hrbeu.edu.cn.
authors.forename: Shi'e
authors.initials: S
authors.lastname: Yang