强间歇干扰下基于黎曼平均的稀疏DOA估计方法  

Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming

作　　者：蓝晓宇 胡吉彦 梁明珅 马爽 LAN Xiaoyu;HU Jiyan;LIANG Mingshen;MA Shuang(College of Electronic Information Engineering,Shenyang Aerospace University,Shenyang 110136,China;Liaoning Provincial Key Laboratory of Aerospace Information Perception and Intelligent Processing,Shenyang 110136,China)

机构地区：[1]沈阳航空航天大学电子信息工程学院,沈阳110136 [2]辽宁省空天信息感知与智能处理重点实验室,沈阳110136

出　　处：《雷达学报(中英文)》2025年第2期280-292,共13页Journal of Radars

基　　金：国家青年科学基金(61801308);航空科学基金(2020Z017054001);辽宁省教育厅面上项目(LJKMZ20220535);辽宁省自然科学基金(2024-MS-135)。

摘　　要：针对复杂电磁环境下雷达干扰增多且靠近强干扰信号的目标信号难以准确估计的问题,该文提出了一种强间歇干扰下基于黎曼平均的稀疏波达方向(DOA)估计方法。首先,在扩展互质阵列接收数据模型下,利用在整个采样周期内目标信号持续活动而强干扰信号间歇性活动的特性,引入黎曼平均对干扰信号进行抑制;然后,将经过处理的数据协方差矩阵向量化,得到虚拟阵列接收数据;最后,在虚拟域中运用稀疏迭代协方差估计(SPICE)算法对稀疏信号进行重构,得到目标信号的DOA估计。仿真结果表明,在信号源数目未知的情况下,该方法可以对角度与强干扰信号紧密相邻的弱目标信号进行高精度的DOA估计。与现有子空间算法和稀疏重构类算法相比,所提算法在较小快拍数和低信噪比下具有更高的估计精度和角度分辨力。Aiming to address the problem of increased radar jamming in complex electromagnetic environments and the difficulty of accurately estimating the target signal close to a strong jamming signal,this paper proposes a sparse Direction of Arrival(DOA)estimation method based on Riemann averaging under strong intermittent jamming.First,under the extended coprime array data model,the Riemann averaging is introduced to suppress the jamming signal by leveraging the property that the target signal is continuously active while the strong jamming signal is intermittently active.Then,the covariance matrix of the processed data is vectorized to obtain virtual array reception data.Finally,the sparse iterative covariance-based estimation method,which is used for estimating the DOA under strong intermittent interference,is employed in the virtual domain to reconstruct the sparse signal and estimate the DOA of the target signal.The simulation results show that the method can provide highly accurate DOA estimation for weak target signals whose angles are closely adjacent to strong interference signals when the number of signal sources is unknown.Compared with existing subspace algorithms and sparse reconstruction class algorithms,the proposed algorithm has higher estimation accuracy and angular resolution at a smaller number of snapshots,as well as a lower signal-to-noise ratio.

关 键 词：波达方向估计 互质阵列 黎曼平均 干扰抑制 稀疏迭代协方差估计 

分 类 号：TN911.7[电子电信—通信与信息系统]