基于快速迭代插值多普勒频率估计的单脉冲前视成像技术

刘可 李悦丽 戴永鹏 金添

刘可, 李悦丽, 戴永鹏, 等. 基于快速迭代插值多普勒频率估计的单脉冲前视成像技术[J]. 雷达学报, 2023, 12(6): 1138–1154. doi: 10.12000/JR23145
引用本文: 刘可, 李悦丽, 戴永鹏, 等. 基于快速迭代插值多普勒频率估计的单脉冲前视成像技术[J]. 雷达学报, 2023, 12(6): 1138–1154. doi: 10.12000/JR23145
LIU Ke, LI Yueli, DAI Yongpeng, et al. Monopulse forward-looking imaging based on Doppler estimation using fast iterative interpolated beamforming algorithm[J]. Journal of Radars, 2023, 12(6): 1138–1154. doi: 10.12000/JR23145
Citation: LIU Ke, LI Yueli, DAI Yongpeng, et al. Monopulse forward-looking imaging based on Doppler estimation using fast iterative interpolated beamforming algorithm[J]. Journal of Radars, 2023, 12(6): 1138–1154. doi: 10.12000/JR23145

基于快速迭代插值多普勒频率估计的单脉冲前视成像技术

doi: 10.12000/JR23145
基金项目: 国家部委基金
详细信息
    作者简介:

    刘 可,硕士生,主要研究方向为雷达智能感知与处理

    李悦丽,博士,教授,主要研究方向为机载雷达合成孔径成像、前视成像以及射频干扰抑制技术

    戴永鹏,博士,讲师,主要研究方向为雷达图像处理及 MIMO阵列设计

    金 添,博士,教授,主要研究方向为新体制雷达系统、智能感知与处理

    通讯作者:

    李悦丽 liyueli4uwb@nudt.edu.cn

  • 责任主编:朱岱寅 Corresponding Editor: ZHU Daiyin
  • 中图分类号: TN958.4

Monopulse Forward-looking Imaging Based on Doppler Estimation Using Fast Iterative Interpolated Beamforming Algorithm

Funds: The National Ministries Foundation
More Information
  • 摘要: 在单脉冲前视成像技术中,同分辨单元内多目标的辨识一直是单脉冲雷达的研究热点。尽管多普勒处理可以提高对前斜视多目标的分辨能力,但在真实目标数量未知、强点目标能量泄露的情况下,多普勒频率的精确估计面临巨大挑战。针对以上问题,该文在单脉冲前视成像中引入具有目标个数估计和单快拍处理能力的快速迭代插值波束形成(FIIB)算法,结合信息论准则估计目标个数,实现对多普勒频率的无偏估计。点目标仿真数值分析结果显示,FIIB对于同分辨单元内的目标数估计和参数估计性能优于调频Z变换(CZT)算法,能实现对±5°外点目标的准确估计。场景仿真和实测数据成像结果表明基于FIIB的单脉冲前视成像算法聚焦能力强,图像对比度更高,并能有效抑制背景杂波。

     

  • 图  1  机载雷达前视成像几何示意图

    Figure  1.  Geometry for forward-looking imaging of a scanning radar

    图  2  强点目标仿真结果(${W_1}/{W_2} = 20\;{\text{dB}}$)

    Figure  2.  Simulation result of strong point targets (${W_1}/{W_2} = 20\;{\text{dB}}$)

    图  3  相邻点目标仿真结果 ($\Delta f = 1/N$)

    Figure  3.  Simulation result of neighboring point targets ($\Delta f = 1/N$)

    图  4  多点目标仿真结果($L = 10$)

    Figure  4.  Simulation result of multiple point targets ($L = 10$)

    图  5  不同迭代次数下同多普勒单元内两个目标的FIIB仿真结果

    Figure  5.  Simulation results of FIIB for two targets situated in a Doppler bin by different iterations

    图  6  点阵目标单脉冲前视成像效果对比

    Figure  6.  Comparison of forward-looking imaging performance

    图  7  距离向1700 m处目标方位向剖面图

    Figure  7.  Azimuthal contour plots for point targets at the range cell 1700 m

    图  8  仿真场景前视成像效果对比

    Figure  8.  Comparison of simulation results in forward-looking imaging

    图  9  实测数据前视成像效果对比

    Figure  9.  Comparison of experimental results in forward-looking imaging

    1  FIIB算法估计多普勒频率流程[18]

    1.   Flowchart of Doppler frequency estimation based on FIIB algorithm[18]

     初始化:
      令 ${\hat f_l} = 0$, ${\hat A_l} = 0$, $l = 1,2, \cdots ,L$
      设 $q = 0$
      ${\boldsymbol{X}} = {\text{FFT}}({\boldsymbol{x}},N)$
     迭代(Q次):
      对于 $l = 1,2, \cdots ,L$
       IF $q = 1$(粗估计):
        $\tilde X(k) = X(k) - \displaystyle\sum \limits_{i = 1,i \ne l}^L {\hat A_i}{\hat S_i}(k),k = 0,1, \cdots ,N - 1$ (i)
        $ {\hat m_l} =\arg \mathop{\max}\limits_{0 \le k \le N - 1 } {\left| {\tilde X(k)} \right|^2} $           (ii)
        ${\hat f_l} = \dfrac{1}{N}{\hat m_l}$                   (iii)
       END IF
       (精估计):
        $ {\tilde X_p}({\hat f_l}) = {X_p}({\hat f_l}) - \displaystyle\sum \limits_{i = 1,i \ne l}^L {\hat A_i}{\hat S_i} \left( {{{\hat f}_l} + \dfrac{p}{N}} \right),p = \pm \dfrac{1}{2} $  (iv)
        其中, ${X_p}({\hat f_l}) = X\left( {{{\hat f}_l} + \dfrac{p}{N}} \right)$          (v)
        $ \delta = \dfrac{1}{2}{{\mathrm{Re}}} \left[ {\dfrac{{{{\tilde X}_{0.5}}({{\hat f}_l}) + {{\tilde X}_{ - 0.5}}({{\hat f}_l})}}{{{{\tilde X}_{0.5}}({{\hat f}_l}) - {{\tilde X}_{ - 0.5}}({{\hat f}_l})}}} \right] $        (vi)
        ${\hat f_l} \leftarrow {\hat f_l} + \dfrac{\delta }{N}$                 (vii)
        $ {\hat A_l} = \dfrac{1}{N} \left\{ {\displaystyle\sum \limits_{k = 0}^{N - 1} x(k){{\text{e}}^{ - {\text{j}}{\textstyle\frac{{2\pi }}{N}}k{{\hat f}_l}}} - \displaystyle\sum \limits_{i = 1,i \ne l}^L {{\hat A}_i}{{\hat S}_i}({{\hat f}_l})} \right\} $ (viii)
      $q \leftarrow q + 1$
     输出: $\left\{ {{{\hat f}_l},{{\hat A}_l}} \right\},l = 1,2, \cdots ,L$
    下载: 导出CSV

    2  具有目标个数估计功能的FIIB算法

    2.   The FIIB algorithm with model order estimation

     初始化:
      设 ${L_{\max }}$, Q
     循环:
      对于 $L = 1,2, \cdots ,{L_{\max }}$
       $\left\{ {{{\hat f}_l},{A_l}} \right\}_{l = 1}^L = {\text{FIIB(}}{\boldsymbol{x}},L,Q)$
       计算 ${C_{{\text{ITC}}}}(L)$
      结束
     取: $ \hat{L}=\mathrm{arg}\mathop{ \mathrm{max}}\limits_{L=1,2,\cdots, {L}_{\mathrm{max}} }\{{C}_{\text{ITC}}(L)\} $
     输出: $\hat L,\{ {\hat f_l},{\hat A_l}\} ,l = 1,2, \cdots ,\hat L$
    下载: 导出CSV

    表  1  点目标仿真参数

    Table  1.   Simulation parameters of point targets

    参数 数值 参数 数值
    频率值${f_1}$ 0.2687 信号长度N 64
    复振幅${A_1}$ 5.0000+3.0000j 迭代次数Q 10
    频率值${f_2}$ 0.3000 设定最大目标数${L_{\max}}$ 5
    复振幅${A_2}$ 0.5000–0.3000j 信噪比$\rho $ (dB) 20
    下载: 导出CSV

    表  2  强点目标参数估计结果(${\boldsymbol{W}}_{\bf{1}}/{\boldsymbol{W}}_{\bf{2}} $=20 dB)

    Table  2.   Estimation result of strong point targets (W1/W2=20 dB)

    参数 方法 目标1 目标2 假目标 假目标 假目标 假目标
    频率 True 0.2687 0.3000 / / / /
    FIIB 0.2687 0.3000 / / / /
    CZT 0.2689 0.2737 0.2578 0.2912 0.3063 0.3232
    FFT 0.2656 / / / / /
    复振幅 True 5.0000+3.0000j 0.5000–0.3000j / / / /
    FIIB 4.9906+3.0169j 0.4905–0.3066j / / / /
    CZT 4.9230+3.1262j 4.6314–1.8004j –1.8767+0.8188j 1.0581–0.5353 0.5413–0.8405j 0.3429–0.4851j
    FFT 2.2325+5.0000j / / / / /
    下载: 导出CSV

    表  3  相邻点目标参数估计结果(${\boldsymbol{\Delta}} {\boldsymbol{f}} = {\bf{1}}/{\boldsymbol{N}}$)

    Table  3.   Estimation result of neighboring point targets (${\boldsymbol{\Delta}} {\boldsymbol{f}} = {\bf{1}}/{\boldsymbol{N}}$)

    参数 方法 目标1 目标2 假目标 假目标 假目标 假目标
    频率 True 0.2844 0.3000 / / / /
    FIIB 0.2844 0.3000 / / / /
    CZT 0.2828 0.3021 0.3049 0.2734 0.3227 0.2550
    FFT 0.2813 0.2969 / / / /
    复振幅 True 5.0000+3.0000j 5.0000–3.0000j / / / /
    FIIB 4.9852+3.0081j 4.9980–3.0238j / / / /
    CZT 4.1085+4.3364j 4.1257–4.3441j 1.5993–5.3751j –1.9113+1.6013j 0.2152–1.7055j –0.5516+0.9638j
    FFT 3.0812+5.0246j 4.9664–0.8527j / / / /
    下载: 导出CSV

    表  4  不同迭代次数下同多普勒单元内两个目标的FIIB参数估计结果

    Table  4.   Estimation result of FIIB for two targets situated in a Doppler bin by different iterations

    迭代次数Q ${f_1}$ ${f_2}$ ${A_1}$ ${A_2}$
    50 0.2899 0.2980 2.4221+2.4827j 7.3413–2.0001j
    100 0.2915 0.2994 4.0880+2.9784j 5.9220–2.9249j
    200 0.2920 0.2999 4.7896+2.9998j 5.2107–2.9917j
    300 0.2922 0.3000 4.9617+2.9887j 5.0442–2.9834j
    (真实值) 0.2922 0.3000 5.0000+3.0000j 5.0000–3.0000j
    下载: 导出CSV

    表  5  前视扫描成像实验仿真参数

    Table  5.   Simulation parameters of a forward-looking scanning radar

    参数 数值 参数 数值
    平台飞行速度(m/s) 100 场景中心地距(m) 1700
    雷达中心频率(GHz) 18 距离×方位分辨单元 (m×m) 3×3
    信号带宽(MHz) 50 和通道3 dB波束宽度(°) 5
    信号脉宽(μs) 1 波束扫描范围(°) –15~15
    脉冲重复频率PRF (Hz) 2000 天线扫描速度(°/s) 30
    下载: 导出CSV

    表  6  场景仿真不同方法的定量评价

    Table  6.   Quantitative evaluation of different methods of scenario simulation

    方法MSEC
    传统单脉冲前视成像$0.00663$$51.4994$
    基于CZT重建多普勒估计的单脉冲成像$0.00394$$52.3441$
    基于FIIB重建多普勒估计的单脉冲成像$0.00387$$ 59.7068 $
    下载: 导出CSV

    表  7  实测数据不同方法的定量评价

    Table  7.   Quantitative evaluation of different methods of real data

    方法$ {\text{ENT}} $C
    实孔径图像$4.2660$$411.9599$
    传统单脉冲前视成像${\text{3}}{\text{.6825}}$$213.4888$
    基于CZT重建多普勒估计的单脉冲成像$3.5228$$233.1903$
    基于FIIB重建多普勒估计的单脉冲成像$2.9651$$292.8872$
    下载: 导出CSV
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  • 收稿日期:  2023-08-29
  • 修回日期:  2023-12-08
  • 网络出版日期:  2023-12-22
  • 刊出日期:  2023-12-28

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