强间歇干扰下基于黎曼平均的稀疏DOA估计方法

蓝晓宇 胡吉彦 梁明珅 马爽

蓝晓宇, 胡吉彦, 梁明珅, 等. 强间歇干扰下基于黎曼平均的稀疏DOA估计方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24175
引用本文: 蓝晓宇, 胡吉彦, 梁明珅, 等. 强间歇干扰下基于黎曼平均的稀疏DOA估计方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24175
LAN Xiaoyu, HU Jiyan, LIANG Mingshen, et al. Sparse DOA estimation method based on riemann averaging under strong intermittent jamming[J]. Journal of Radars, in press. doi: 10.12000/JR24175
Citation: LAN Xiaoyu, HU Jiyan, LIANG Mingshen, et al. Sparse DOA estimation method based on riemann averaging under strong intermittent jamming[J]. Journal of Radars, in press. doi: 10.12000/JR24175

强间歇干扰下基于黎曼平均的稀疏DOA估计方法

DOI: 10.12000/JR24175
基金项目: 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)
详细信息
    作者简介:

    蓝晓宇,博士,副教授,主要研究方向为雷达阵列信号处理

    胡吉彦,硕士生,主要研究方向为雷达阵列信号处理

    梁明珅,博士,讲师,主要研究方向为雷达阵列信号处理

    马 爽,博士,副教授,主要研究方向为阵列天线设计,阵列信号处理

    通讯作者:

    胡吉彦 1534018950@qq.com

  • 责任主编:廖桂生 Corresponding Editor: LIAO Guisheng
  • 中图分类号: TN911.7

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

Funds: National Science Foundation for Young Scientists of China (61801308), Aeronautical Science Foundation (2020Z017054001), General Program of the Education Department of Liaoning Province(LJKMZ20220535), Natural Science Foundation of Liaoning Province of China (2024-MS-135)
More Information
  • 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,利用目标信号持续活动而强干扰信号间歇活动的特性,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。

     

  • 图  1  扩展互质阵列结构示意图

    Figure  1.  Illustration of the extended coprime array structure

    图  2  连续信号和间歇性干扰信号活动示意图

    Figure  2.  Schematic diagram of continuous and intermittent interfering signal activity

    图  3  3种算法的空间谱图

    Figure  3.  Spatial spectrum of the three algorithms

    图  4  不同算法分辨性能对比

    Figure  4.  Comparison of the resolution performance of different algorithms

    图  5  不同算法下相干信号空间谱对比

    Figure  5.  Comparison of spatial spectrum of coherent signals under different algorithms

    图  6  不同参数条件下的DOA估计精度

    Figure  6.  Accuracy of DOA estimation under different parameter conditions

    图  7  不同参数条件下的成功检测概率

    Figure  7.  Successful resolution under different parameter conditions

    图  8  算法估计精度和分辨率随快拍数的变化

    Figure  8.  Algorithmic estimation of accuracy and resolution as a function of number of snapshots

    图  9  所提算法在不同参数条件下的DOA估计精度

    Figure  9.  Accuracy of DOA estimation of the proposed algorithm under different parameter conditions

    1  基于黎曼平均的虚拟域SPICE算法

    1.   A virtual domain SPICE algorithm based on Riemannian averaging

     输入:协方差矩阵的黎曼均值${\hat {\boldsymbol{R}}_{\mathrm{R}}}$,过完备网格点${\boldsymbol{\varTheta}} $,迭代终止阈值${\delta _{\min }}$,最大迭代次数Q
     输出:空间谱${\boldsymbol{P}}_{\mathrm{S}}^{(i)}$
     1 根据式(22)得到虚拟阵列数据r并通过式(23)剔除r中重复元素得到$\tilde {\boldsymbol{r}}$;
     2 初始化:初始迭代次数$i = 1$,初始阈值${\delta _0} = 1$,分别根据式(35)、式(36)、式(24)和式(34)依次初始化$p_c^{(0)}$, ${\boldsymbol{P}}_{\mathrm{S}}^{(i)}$, R和${\rho ^0}$,$\sigma _n^2$初始化为
     $ \sigma _n^2 = \min \left\{ {p_c^{(0)}} \right\} $;
     3 迭代开始:根据式(32)和式(33)更新$p_c^{(i + 1)}$和$\sigma _n^{(i + 1)}$;
     4 根据式(36)、式(24)和式(34)依次更新${\boldsymbol{P}}_{\mathrm{S}}^{(i)}$、${{\boldsymbol{R}}^{(i)}}$和${\rho ^{(i)}}$;
     5 根据式(28)更新$\delta $,如果$\delta > {\delta _{\min }}$且$i < Q$,则返回步骤3;
     迭代结束
    下载: 导出CSV

    表  1  算法计算复杂度对比表

    Table  1.   Comparison table of computational complexity of algorithms

    算法复杂度
    SS-MUSIC算法$ O(L{G^2} + 2{(MN + 1)^3} + C{(MN + 1)^2} - CK(MN + 1)) $
    SS-SPICE算法$ O({G^3}CQ + G{C^{^2}}Q + L{G^2}) $
    SS-EQSPICE算法$ O({G^3}CQ' + G{C^{^2}}Q' + L{G^2}) $
    本文所提算法$ O({G^3}CQ + G{C^{^2}}Q + L{G^2} + {G^3} + {G^2}) $
    下载: 导出CSV
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  • 收稿日期:  2024-08-31
  • 修回日期:  2024-11-10

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