可信推断近场稀疏综合阵列三维毫米波成像

杨磊 霍鑫 申瑞阳 宋昊 胡仲伟

杨磊, 霍鑫, 申瑞阳, 等. 可信推断近场稀疏综合阵列三维毫米波成像[J]. 雷达学报(中英文), 2024, 13(5): 1092–1108. doi: 10.12000/JR24097
引用本文: 杨磊, 霍鑫, 申瑞阳, 等. 可信推断近场稀疏综合阵列三维毫米波成像[J]. 雷达学报(中英文), 2024, 13(5): 1092–1108. doi: 10.12000/JR24097
YANG Lei, HUO Xin, SHEN Ruiyang, et al. Credible inference of near-field sparse array synthesis for three-dimensional millimeter-wave imagery[J]. Journal of Radars, 2024, 13(5): 1092–1108. doi: 10.12000/JR24097
Citation: YANG Lei, HUO Xin, SHEN Ruiyang, et al. Credible inference of near-field sparse array synthesis for three-dimensional millimeter-wave imagery[J]. Journal of Radars, 2024, 13(5): 1092–1108. doi: 10.12000/JR24097

可信推断近场稀疏综合阵列三维毫米波成像

DOI: 10.12000/JR24097 CSTR: 32380.14.JR24097
基金项目: 国家自然科学基金(62271487),中央高校基本科研业务费(3122023PT04)
详细信息
    作者简介:

    杨 磊,博士,教授,博士生导师,主要研究方向为高分辨SAR成像及机器学习理论应用

    霍 鑫,博士生,主要研究方向为毫米波成像与稀疏阵列构型设计

    申瑞阳,硕士生,主要研究方向为毫米波成像与稀疏阵列构型设计

    宋 昊,硕士生,主要研究方向为毫米波成像与稀疏阵列构型设计

    胡仲伟,博士,讲师,硕士生导师,主要研究方向为高分辨SAR成像及优化学习理论

    通讯作者:

    胡仲伟 zwhu@cauc.edu.cn

    杨磊 yanglei840626@163.com

  • 责任主编:邓彬 Corresponding Editor: DENG Bin
  • 中图分类号: TN957

Credible Inference of Near-field Sparse Array Synthesis for Three-dimensional Millimeter-wave Imagery

Funds: The National Natural Science Foundation of China (62271487), Fundamental Research Funds for the Central Universities (3122023PT04)
More Information
  • 摘要: 考虑到主动式电扫描毫米波成像系统在实际应用中成像场景要求大,分辨率要求高,但毫米波的波长短,继而造成满足奈奎斯特采样定理的均匀阵列规模及馈电网络复杂度过高,面临着成像精度、成像速度和系统成本之间的矛盾。针对以上问题,该文提出了可信推断近场稀疏综合阵列算法(CBI-SAS),在全贝叶斯学习框架下,该算法基于贝叶斯推断对复激励权值进行稀疏优化,得到复激励权值的完全统计后验概率密度函数,从而利用其高阶统计信息得到复激励权值的最优值及其置信区间和置信度。在贝叶斯推断中,为了实现较少数量的阵元合成期望波束方向图,可通过对复值激励权值引入重尾的拉普拉斯稀疏先验。然而,由于先验概率模型与参考方向图数据模型非共轭,因此需对先验模型进行分层贝叶斯建模,从而保证得到的复激励权值完全后验分布具有闭合解析解。为了避免求解完全后验分布的高维积分,采用变分贝叶斯期望最大化方法计算复激励权值后验概率密度函数,实现复激励权值的可信推断。仿真模拟实验结果显示,相较于传统稀疏阵列合成方法,所提方法阵元稀疏度更低、归一化均方误差更小、匹配方向图精度更好。此外,基于设计的稀疏阵列采集近场一维电扫和二维平面全电扫实测回波数据后,利用改进三维时域算法进行三维重建,验证了所提CBI-SAS算法在保证成像结果的同时降低了系统复杂性的优势。

     

  • 图  1  毫米波圆周扫描成像系统几何模型

    Figure  1.  Geometric model of millimeter-wave electric scanning circumferential 3D imaging system

    图  2  任意方位下近场线阵聚焦模型

    Figure  2.  Focusing model of near-field linear array in Cartesian coordinate system with arbitrary orientation

    图  3  贝叶斯分层概率模型

    Figure  3.  Bayesian graphic model

    图  4  近场稀疏阵列综合算法流程图

    Figure  4.  Flow chart of near-field sparse array synthesis algorithm

    图  5  第1阶段的第u个子孔径的局部极坐标网格

    Figure  5.  The first stage of the u subaperture of the local polar grid

    图  6  二维水平切面网格图

    Figure  6.  Two-dimensional horizontal section grid diagram

    图  7  不同算法下的近场天线方向图

    Figure  7.  The direction pattern of near-field antenna and the position and amplitude of near-field array under different algorithms

    图  8  不同算法下的近场阵元位置及其幅度

    Figure  8.  The position and amplitude of near-field array under different algorithms

    图  9  不同聚集线位置下不同算法匹配参考方向图的均方误差对比图

    Figure  9.  Different algorithms match the mean square error comparison graph of reference direction graph under different aggregation line positions

    图  10  近场阵元位置及幅度的置信区间

    Figure  10.  Confidence interval for the position and amplitude of near-field elements

    图  11  相同稀疏度下不同方法设计的圆周线性稀疏阵列获得的成像结果对比

    Figure  11.  Comparison of imaging results obtained by circular linear sparse arrays designed by different methods under the same sparsity

    图  12  相同稀疏度下不同方法设计的全电扫平面稀疏阵列获得的成像结果对比

    Figure  12.  Comparison of imaging reaults obtained by full scanned planar sparse arrays designed by different methods under the same sparsity

    图  13  方位15°时不同方法设计的圆周线性稀疏阵列侧面成像结果对比

    Figure  13.  Comparison of side imaging results of circular linear sparse array designed by different methods at 15° orientation

    图  14  边缘点A的位置示意图

    Figure  14.  Diagram of the position of edge point A

    图  15  不同稀疏阵列综合算法成像结果边缘点的剖面图

    Figure  15.  Profile of edge points of imaging results of different sparse array synthesis algorithms

    表  1  不同稀疏阵列合成算法下的性能对比

    Table  1.   Performance comparison of different sparse array synthesis algorithms

    方法 峰值旁瓣电平(dB) 阵元个数 稀疏度(%) 均方误差
    本文所提方法 –15.81 273 71.28 1.89×10–4
    ${\ell _1}$范数 –15.76 339 88.51 3.50×10–3
    FOCUSS法 –15.68 321 83.81 3.08×10–4
    下载: 导出CSV

    表  2  毫米波电扫描圆周成像系统相关参数

    Table  2.   Parameters of milli-meter wave electrical scanning circumferential imaging system

    参数数值
    系统工作带宽6.5 GHz
    工作频率27 GHz
    目标距离0.4~0.8 m
    方位/俯仰角55°/55°
    采样点数64
    阵元间距0.0052 m
    旋转次数314
    单次旋转角度0.2867°
    旋转半径0.628 m
    下载: 导出CSV

    表  3  不同稀疏阵列综合方法成像结果边缘点的剖面图定量分析

    Table  3.   Quantitative analysis of profile of edge points of imaging results by different sparse array synthesis methods

    边缘点的成像结果 高度向峰值旁瓣比(dB) 3 dB宽度时高度
    向分辨率(mm)
    方位向峰值旁瓣比(dB) 3 dB宽度时方位
    向分辨率(mm)
    均匀阵列成像 –24.69 7.76 –8.01 4.2
    本文所提算法稀疏阵列成像 –22.15 7.76 –8.01 4.2
    基于FOCUSS算法稀疏阵列成像 –20.18 7.79 –8.01 4.2
    基于凸优化CVX求解的${\ell _1}$范数稀疏阵列成像 –17.98 7.82 –8.01 4.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-05-22
  • 修回日期:  2024-08-05
  • 网络出版日期:  2024-08-26
  • 刊出日期:  2024-09-28

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