基于遗传算法的无人机载穿墙三维SAR航迹规划方法

杨小鹏 马忠杰 钟世超 渠晓东 曾小路

杨小鹏, 马忠杰, 钟世超, 等. 基于遗传算法的无人机载穿墙三维SAR航迹规划方法[J]. 雷达学报(中英文), 2024, 13(4): 731–746. doi: 10.12000/JR24068
引用本文: 杨小鹏, 马忠杰, 钟世超, 等. 基于遗传算法的无人机载穿墙三维SAR航迹规划方法[J]. 雷达学报(中英文), 2024, 13(4): 731–746. doi: 10.12000/JR24068
YANG Xiaopeng, MA Zhongjie, ZHONG Shichao, et al. Trajectory planning method for UAV-through-the-wall 3D SAR based on a genetic algorithm[J]. Journal of Radars, 2024, 13(4): 731–746. doi: 10.12000/JR24068
Citation: YANG Xiaopeng, MA Zhongjie, ZHONG Shichao, et al. Trajectory planning method for UAV-through-the-wall 3D SAR based on a genetic algorithm[J]. Journal of Radars, 2024, 13(4): 731–746. doi: 10.12000/JR24068

基于遗传算法的无人机载穿墙三维SAR航迹规划方法

DOI: 10.12000/JR24068
基金项目: 国家自然科学基金(62101042)
详细信息
    作者简介:

    杨小鹏,教授,主要研究方向为相控阵雷达及自适应阵列信号处理、探地雷达技术、穿墙雷达技术

    马忠杰,硕士生,主要研究方向为无人机载穿墙合成孔径雷达成像

    钟世超,博士,主要研究方向为无人机载穿墙雷达运动误差补偿、建筑物结构布局成像

    渠晓东,博士,副研究员,主要研究方向为遮蔽空间动目标定位跟踪、行为识别与姿态重构

    曾小路,博士,副研究员,主要研究方向为穿墙雷达静止目标成像、智能无线感知与物联网技术

    通讯作者:

    钟世超 zhongshichao16@bit.edu.cn

  • 责任主编:李小龙 Corresponding Editor: LI Xiaolong
  • 中图分类号: TN957.52

Trajectory Planning Method for UAV-through-the-wall 3D SAR Based on a Genetic Algorithm

Funds: The National Natural Science Foundation of China (62101042)
More Information
  • 摘要: 传统手持或车载穿墙雷达由于架设高度受限,无法对城市高层建筑内部目标进行透视成像,无人机载穿墙雷达具有灵活机动、高效便捷、无高度限制等优势,可对城市高层楼宇进行大范围三维穿透探测。三维层析合成孔径雷达(SAR)成像广泛采用多基线扫描模式,以获得高度向高分辨能力,但存在高度向空域欠采样导致的栅瓣问题。对此,该文提出一种基于遗传算法的无人机载穿墙三维SAR航迹规划方法,通过非均匀化飞行航迹,削弱周期性的雷达回波能量叠加,从而抑制栅瓣、实现更优的成像质量。该算法结合飞行距离与无人机载穿墙雷达成像质量的内在关系,建立了无人机航迹规划代价函数;利用遗传算法对3种典型的无人机飞行轨迹关键控制点进行基因编码,并进行基因杂交、变异等以优化种群与个体;最终通过最小化代价函数,分别筛选出3种典型飞行模式下的最优飞行航迹。仿真和实测数据的三维成像结果表明:相较于传统等间距多基线飞行模式,所提方法显著抑制了成像目标的栅瓣效应;此外,无人机斜线飞行的航迹长度明显缩短,提高了无人机载穿墙SAR成像效率。

     

  • 图  1  无人机载穿墙雷达高层建筑探测场景示意图

    Figure  1.  Schematic diagram of detection scenario for UAV-TWR in high-rise buildings

    图  2  栅瓣场景示意图

    Figure  2.  Schematic diagram of grating scene

    图  3  UMB-SAR基因型

    Figure  3.  UMB-SAR genotype

    图  4  CF-SAR基因型

    Figure  4.  CF-SAR genotype

    图  5  OF-SAR基因型

    Figure  5.  OF-SAR genotype

    图  6  三维穿墙仿真场景

    Figure  6.  Through-the-wall 3D simulation scene

    图  7  MB-SAR航迹与成像结果

    Figure  7.  MB-SAR trajectory and imaging results

    图  8  UMB-SAR航迹优化与成像结果

    Figure  8.  UMB-SAR trajectory optimization and imaging results

    图  9  Z-SAR航迹优化与成像结果

    Figure  9.  Z-SAR trajectory optimization and imaging results

    图  10  CF-SAR航迹优化与成像结果

    Figure  10.  CF-SAR trajectory optimization and imaging results

    图  11  OF-SAR航迹优化与成像结果

    Figure  11.  OF-SAR trajectory optimization and imaging results

    图  12  CEM200型号无人机载穿墙雷达

    Figure  12.  UAV-TWR CEM200

    图  13  穿墙场景测试图

    Figure  13.  Measurement of through-the-wall scene

    图  14  实测穿墙场景下不同模式的无人机飞行航迹

    Figure  14.  Actual measurement of UAV flight trajectory in different modes in through-the-wall scenarios

    图  15  理想与实测航迹$ {R_{\rm{pgl}}} $仿真对比

    Figure  15.  Comparison of $ {R_{\rm{pgl}}} $ between ideal and real trajectory in simulation

    图  16  穿墙场景下4种模式的成像情况(“Ⅰ”表示距离-高度向截面,“Ⅱ”表示方位-高度向截面)

    Figure  16.  Imaging situation of four modes in through-the-wall scenario (“Ⅰ” represents the range-height section, “Ⅱ” represents the azimuth-height section)

    表  1  穿墙场景数值仿真参数

    Table  1.   Simulation parameter settings

    参数 数值
    雷达载频 2.95 GHz
    雷达带宽 440 MHz
    场景方位向范围 0~10 m
    场景高度向范围 0~5 m
    场景距离向范围 0~10 m
    墙体厚度 0.2 m
    墙体相对介电常数 4.0
    点目标1坐标 (8.0 m, 7.0 m, 2.7 m)
    点目标2坐标 (5.0 m, 5.0 m, 2.0 m)
    点目标3坐标 (2.0 m, 6.0 m, 3.7 m)
    超参数权重$ ({w_1},{w_2},{w_3}) $ (0.02, 0.43, 0.55)
    下载: 导出CSV

    表  2  算法仿真结果对比

    Table  2.   Comparison of algorithm simulation results

    飞行模式代价函数$ {R_{\rm{pgl}}} $(dB)
    MB-SAR\−0.51
    UMB-SAR−3.15−8.64
    Z-SAR−3.68−9.73
    CF-SAR−4.21−10.56
    OF-SAR−5.25−11.60
    下载: 导出CSV

    表  3  实测数据参数

    Table  3.   Actual measured data parameters

    实验参数 数值
    雷达载频 2.95 GHz
    脉冲重复频率 1923 Hz
    雷达带宽 440 MHz
    采样率 10 MHz
    飞行速度 2 m/s
    下载: 导出CSV

    表  4  算法实测结果对比(dB)

    Table  4.   Comparison of algorithm measured results (dB)

    算法 $ {R_{\rm{pgl}}} $
    MB-SAR −1.91
    UMB-SAR −4.14
    CF-SAR −7.71
    OF-SAR −9.52
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-04-19
  • 修回日期:  2024-06-13
  • 网络出版日期:  2024-07-02
  • 刊出日期:  2024-08-28

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