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), 待出版. 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), 待出版. 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, in press. 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, in press. 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

    Table  4.   Comparison of algorithm measured results

    算法 $ {R_{\rm{pgl}}} $(dB)
    MB-SAR −1.91
    UMB-SAR −4.14
    CF-SAR −7.71
    OF-SAR −9.52
    下载: 导出CSV
  • [1] GAO Weicheng, YANG Xiaopeng, QU Xiaodong, et al. TWR-MCAE: A data augmentation method for through-the-wall radar human motion recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5118617. doi: 10.1109/TGRS.2022.3213748.
    [2] YANG Xiaopeng, GAO Weicheng, QU Xiaodong, et al. A lightweight multiscale neural network for indoor human activity recognition based on macro and micro-Doppler features[J]. IEEE Internet of Things Journal, 2023, 10(24): 21836–21854. doi: 10.1109/JIOT.2023.3301519.
    [3] 金添, 宋勇平, 崔国龙, 等. 低频电磁波建筑物内部结构透视技术研究进展[J]. 雷达学报, 2021, 10(3): 342–359. doi: 10.12000/JR20119.

    JIN Tian, SONG Yongping, CUI Guolong, et al. Advances on penetrating imaging of building layout technique using low frequency radio waves[J]. Journal of Radars, 2021, 10(3): 342–359. doi: 10.12000/JR20119.
    [4] UNAL M, CALISKAN A, TURK A S, et al. Subsurface and through-wall SAR imaging techniques for ground penetrating radar[J]. Технология и Конструирование в Электронной Аппаратуре, 2013(6): 32–36. doi: 10.15222/tkea2013.6.32.
    [5] WANG Yazhou and FATHY A E. Advanced system level simulation platform for three-dimensional UWB through-wall imaging SAR using time-domain approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1986–2000. doi: 10.1109/tgrs.2011.2170694.
    [6] SÉVIGNY P. Joint through-wall 3-D radar imaging and motion detection using a stop-and-go SAR trajectory[C]. 2016 IEEE Radar Conference, Philadelphia, USA, 2016: 1–5. doi: 10.1109/RADAR.2016.7485325.
    [7] LIU Jiangang, JIA Yong, KONG Lingjiang, et al. MIMO through-wall radar 3-D imaging of a human body in different postures[J]. Journal of Electromagnetic Waves and Applications, 2016, 30(7): 849–859. doi: 10.1080/09205071.2016.1159996.
    [8] KONG Lingjiang, CUI Guolong, YANG Xiaobo, et al. Three-dimensional human imaging for through-the-wall radar[C]. 2009 IEEE Radar Conference, Pasadena, USA, 2009: 1–4. doi: 10.1109/RADAR.2009.4976932.
    [9] ZHAO Yikun, YANG Wenfu, LI Yinchuan, et al. Multi-path suppression algorithm for through-the-wall imaging[J]. The Journal of Engineering, 2019, 2019(19): 5629–5633. doi: 10.1049/joe.2019.0126.
    [10] FREY O and MEIER E. 3-D time-domain SAR imaging of a forest using airborne multibaseline data at L- and P-bands[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3660–3664. doi: 10.1109/tgrs.2011.2128875.
    [11] DOGARU T, PHELAN B, and LIAO Dahan. Imaging of buried targets using UAV-based, ground penetrating, synthetic aperture radar[C]. SPIE 11003, Radar Sensor Technology XXIII, Baltimore, USA, 2019. doi: 10.1117/12.2519116.
    [12] ANDRE D, FAULKNER B, and FINNIS M. Low-frequency 3D synthetic aperture radar for the remote intelligence of building interiors[J]. Electronics Letters, 2017, 53(15): 984–987. doi: 10.1049/el.2017.1584.
    [13] 廖明生, 魏恋欢, 汪紫芸, 等. 压缩感知在城区高分辨率SAR层析成像中的应用[J]. 雷达学报, 2015, 4(2): 123–129. doi: 10.12000/JR15031.

    LIAO Mingsheng, WEI Lianhuan, WANG Ziyun, et al. Compressive sensing in high-resolution 3D SAR tomography of urban scenarios[J]. Journal of Radars, 2015, 4(2): 123–129. doi: 10.12000/JR15031.
    [14] ALISTARH C A, PODILCHAK S K, RE P D H, et al. Sectorized FMCW MIMO radar by modular design with non-uniform sparse arrays[J]. IEEE Journal of Microwaves, 2022, 2(3): 442–460. doi: 10.1109/jmw.2022.3165401.
    [15] FENG Chen, YE Haojian, HONG Hong, et al. A hybrid algorithm for sparse antenna array optimization of MIMO radar[C]. 2022 IEEE Radio and Wireless Symposium, Las Vegas, USA, 2022: 115–117. doi: 10.1109/RWS53089.2022.9719968.
    [16] HARTMANN F and OSTERMANN J. Investigation of the effect of the flight path on the three dimensional locatability of targets[C]. 2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Bali, Indonesia, 2021: 1–6. doi: 10.1109/APSAR52370.2021.9688372.
    [17] BROWN A and ANDERSON D. Trajectory optimization for high-altitude long-endurance UAV maritime radar surveillance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(3): 2406–2421. doi: 10.1109/taes.2019.2949384.
    [18] DROZDOWICZ J and SAMCZYNSKI P. Drone-based 3D synthetic aperture radar imaging with trajectory optimization[J]. Sensors, 2022, 22(18): 6990. doi: 10.3390/s22186990.
    [19] SAEEDI J and FAEZ K. A back-projection autofocus algorithm based on flight trajectory optimization for synthetic aperture radar imaging[J]. Multidimensional Systems and Signal Processing, 2016, 27(2): 411–431. doi: 10.1007/s11045-014-0308-1.
    [20] JIAO Bowen, WANG Zuyi, and XU Li. Control strategy and flight trajectory optimization strategy based on improved De Casteljau’s algorithm for indoor drone[C]. 2021 33rd Chinese Control and Decision Conference (CCDC), Kunming, China, 2021: 4633–4638. doi: 10.1109/CCDC52312.2021.9602413.
    [21] LAHMERI M A, GHANEM W, KNILL C, et al. Trajectory and resource optimization for UAV synthetic aperture radar[C]. 2022 IEEE Globecom Workshops (GC Wkshps), Rio de Janeiro, Brazil, 2022: 897–903. doi: 10.1109/GCWkshps56602.2022.10008658.
    [22] TASHTARIAN G and MAJEDI M S. Grating lobes reduction in linear arrays composed of subarrays using PSO[C]. 2019 International Symposium on Networks, Computers and Communications (ISNCC), Istanbul, Turkey, 2019: 1–6. doi: 10.1109/ISNCC.2019.8909108.
    [23] INDU N, SINGH R P, CHOUDHARY H R, et al. Trajectory design for UAV-to-ground communication with energy optimization using genetic algorithm for agriculture application[J]. IEEE Sensors Journal, 2021, 21(16): 17548–17555. doi: 10.1109/jsen.2020.3046463.
    [24] 王楚涵, 李小龙, 望明星, 等. 一种机载分布式MIMO雷达节点位置与路径分步优化管控方法[J/OL]. 信号处理, 2024: 1–23. http://kns.cnki.net/kcms/detail/11.2406.TN.20231114.1512.004.html, 2024.

    WANG Chuhan, LI Xiaolong, WANG Mingxing, et al. A stepwise optimization and control method for node location and path of airborne distributed MIMO radar[J/OL]. Journal of Signal Processing, 2024: 1–23. http://kns.cnki.net/kcms/detail/11.2406.TN.20231114.1512.004.html, 2024.
    [25] WANG Xiaofeng, RUAN Yaduan, and ZHANG Xinggan. Accuracy improvement of high-resolution wide-swath spaceborne synthetic aperture radar imaging with low pule repetition frequency[J]. Remote Sensing, 2023, 15(15): 3811. doi: 10.3390/rs15153811.
    [26] WARREN C, GIANNOPOULOS A, GRAY A, et al. A CUDA-based GPU engine for gprMax: Open source FDTD electromagnetic simulation software[J]. Computer Physics Communications, 2019, 237: 208–218. doi: 10.1016/j.cpc.2018.11.007.
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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

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