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用于稳健自适应波束形成的具有概率正则化的二阶锥规划

Second-order cone programming with probabilistic regularization for robust adaptive beamforming.

作者信息

Guo Xijing, Miron Sebastian, Yang Yixin, Yang Shi'e

机构信息

Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China

Université de Lorraine, Centre de Recherche en Automatique de Nancy, Unité Mixte de Recherche 7039, Vandœuvre-lès-Nancy, F-54506, France

出版信息

J Acoust Soc Am. 2017 Mar;141(3):EL199. doi: 10.1121/1.4976846.

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.

摘要

引入概率正则化(PR)以使超指向性阵列波束形成对传感器特性失配具有鲁棒性。目标是在以高概率确保鲁棒性的同时扩大方向性。通过二阶锥规划解决PR问题,其中基于蒙特卡罗模拟通过对系统扰动的统计分析来选择正则化参数。在未校准的小型3×3均匀矩形阵列上进行实验。结果表明,对于这种特定阵列,PR方法对传感器失配具有鲁棒性,并且与其他鲁棒自适应波束形成方法相比实现了更高水平的方向性。

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