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导向向量随机误差修正的稳健波束形成
引用本文:余赟,凌青,陈阳,郭昆,虞飞.导向向量随机误差修正的稳健波束形成[J].声学学报,2023,48(2):312-318.
作者姓名:余赟  凌青  陈阳  郭昆  虞飞
作者单位:1.海军研究院 北京 100161
摘    要:当导向向量同时存在导向偏差和随机误差引起的畸变时,现有针对导向偏差的稳健波束形成所估计的导向向量偏离真实值。针对该问题,提出了导向向量随机误差修正的稳健波束形成方法。将导向向量分为预定义观察扇面特征子空间内外两部分,通过导向向量估计的稳健波束形成方法估计导向向量在预定义观察扇面特征子空间内的分量,在此基础上进一步增加不确定集约束,对导向向量在预定义观察扇面特征子空间外的分量进行修正,从而提高导向向量的估计精度。理论与仿真研究表明:与针对导向偏差的稳健波束形成相比,在同时存在导向偏差和随机误差的情况下,所提方法输出信干噪比不变,而其导向向量估计精度更高,导向向量波束响应的峰值是目标方位的无偏估计。进一步的海试数据分析表明:所提方法在低信噪比下较已有方法具有更好的稳健性。

关 键 词:水声阵列  稳健波束形成  导向向量  随机误差  导向偏差  特征空间正交分解
收稿时间:2021-05-13

Steering vector random error modification robust beamforming
Institution:1.Naval Research Academy Beijing 1001612.School of Microelectronics and Control Engineering, Changzhou University Changzhou 2131643.College of Underwater Acoustic Engineering, Harbin Engineering University Harbin 150001
Abstract:The steering vectors estimated by the existing beamforming methods generally depart from their ground-truth values when they are influenced by both steering deviation and random error. To solve this problem, a steering vector random error modification robust beamforming method is proposed in this paper. The steering vector is divided into both inside and outside parts of the pre-defined sector feature subspace, and then the robust beamforming for steering deviation is employed to estimate the component of the steering vector inside the pre-defined sector feature subspace. After that, the uncertainty set constraint is further added to estimate the component of the steering vector outside the pre-defined observation subspace, which then brings high accuracy in steering vector estimation. Simulation results indicate that the proposed method possesses a more stable signal-to-interference-plus-noise ratio and higher steering vector estimation accuracy than the existing robust beamforming methods under both steering deviation and random error. In addition, the peak of the steering vector beam response is the unbiased estimation of the target direction. Furthermore, sea-trial results illustrate the outstanding robustness of the proposed method under low signal-to-noise-ratio conditions. 
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