移动强干扰条件下大孔径阵列稀疏空间谱估计  

Sparse spatial spectrum estimation for large aperture array under moving strong interfere

作　　者：唐灿 张波 李风华[1] TANG Can;ZHANG Bo;LI Fenghua(State Key Laboratory of Acoustics,Institute of Acoustics,Chinese Academy of Sciences,Beijing,100190;University of Chinese Academy of Sciences,Beijing,100049)

机构地区：[1]中国科学院声学研究所声场声信息国家重点实验室,北京100190 [2]中国科学院大学,北京100049

出　　处：《声学学报》2024年第4期696-708,共13页Acta Acustica

基　　金：国家自然科学基金项目(11974017)资助。

摘　　要：无源声呐阵列的目标方位估计性能受到强干扰的显著影响,在干扰源快速移动时大孔径阵列还可能存在快拍不足的问题,为此提出一种结合波束域预处理以实现干扰抑制和数据降维的稀疏空间谱估计方法。首先在预处理阶段对干扰方向形成自适应零点,并通过协方差矩阵锥化展宽零点,然后对预处理后的波束域数据应用稀疏贝叶斯学习进行空间谱估计。与现有方法相比,波束域预处理可以有效减少阻带范围内干扰声源的影响,提高通带范围内空间谱估计的精度和计算效率,零点展宽可以进一步加强稳健性,避免出现快拍不足问题。仿真分析和海试结果表明,所提方法在移动强干扰条件下仍能准确估计目标声源的多途到达角,且计算时间远低于阵元域稀疏方法。The performance of direction of arrival(DOA)estimation for targets using passive sonar arrays is significantly affected by strong interferers.Moreover,large-aperture arrays also encounter the snapshot-deficient problem when interferers move rapidly.To address these issues,a new sparse spatial spectrum estimation method is proposed.This method combines beam-space preprocessing to achieve interference suppression and reduce data dimensionality.During the preprocessing stage,adaptive nulls are initiated in the interferer direction and subsequently broadened with covariance matrix tapers.Sparse Bayesian learning is then applied to the preprocessed beam-space data to estimate the spatial spectrum.Compared to existing methods,beam-space preprocessing significantly reduces the impact of interferers in the stopband while enhancing the accuracy and computational efficiency of spatial spectrum estimation in the passband.Null broadening further strengthens robustness and avoids the snapshotdeficient problem.Simulation and experimental results demonstrate the capability of the proposed method to accurately estimate the multipath arrival angle of the target source even under moving strong interferers.Furthermore,the computational time required by the proposed approach is substantially lower than that of the element-space sparse method.

关 键 词：大孔径阵列 空间谱估计 波束域预处理 零点展宽 稀疏贝叶斯学习 

分 类 号：U666.7[交通运输工程—船舶及航道工程]