多径利用雷达目标探测技术综述与展望

孔令讲,  郭世盛,  陈家辉,  吴佩仑,  崔国龙

文章导航 > 雷达学报  > 2024 >  13(1): 23-45
孔令讲, 郭世盛, 陈家辉, 等. 多径利用雷达目标探测技术综述与展望[J]. 雷达学报(中英文), 2024, 13(1): 23–45. doi: 10.12000/JR23134
引用本文: 孔令讲, 郭世盛, 陈家辉, 等. 多径利用雷达目标探测技术综述与展望[J]. 雷达学报(中英文), 2024, 13(1): 23–45. doi: 10.12000/JR23134
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KONG Lingjiang, GUO Shisheng, CHEN Jiahui, et al. Overview and prospects of multipath exploitation radar target detection technology[J]. Journal of Radars, 2024, 13(1): 23–45. doi: 10.12000/JR23134
Citation: KONG Lingjiang, GUO Shisheng, CHEN Jiahui, et al. Overview and prospects of multipath exploitation radar target detection technology[J]. Journal of Radars, 2024, 13(1): 23–45. doi: 10.12000/JR23134
shu

多径利用雷达目标探测技术综述与展望

DOI: 10.12000/JR23134

  • 电子科技大学信息与通信工程学院 成都 611731

基金项目: 国家自然科学基金(62001091)
详细信息
    作者简介:

    孔令讲，博士，教授，研究方向包括新体制雷达、统计信号处理、优化理论和算法、雷达信号处理、非合作信号处理技术和自适应阵列信号处理及城市环境目标探测等

    郭世盛，博士，副研究员，研究方向包括城市环境目标探测、基于雷达的人体行为识别等

    陈家辉，博士，主要研究方向包括城市环境目标探测和无线层析成像等

    吴佩仑，博士生，研究方向包括超宽带雷达成像和城市环境目标探测

    崔国龙，博士，教授，研究方向包括最优化理论和算法、雷达目标检测理论、波形多样性以及城市环境目标探测等

    通讯作者:

    郭世盛 ssguo@uestc.edu.cn

  • 责任主编：金添 Corresponding Editor: JIN Tian

  • 中图分类号: TN957

Overview and Prospects of Multipath Exploitation Radar Target Detection Technology

  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62001091)
More Information

    摘要
  • 摘要: 多径利用雷达(MER)目标探测技术主要基于电磁波在介质表面的反射、衍射等非直视(NLOS)多路径传播特性，实现对城市街角、车辆遮挡等“视觉”盲区内隐蔽目标的有效探测，其能够为城市作战、智能驾驶等多种应用提供服务，具有重要的现实意义和研究价值。为获知该领域的发展脉络，并预测未来可能的发展趋势，该文对21世纪初以来该领域国内外公开文献进行了归纳总结。相关文献的梳理结果表明，根据探测平台类型的不同，多径利用雷达目标探测技术目前主要包括两类：基于空中平台的多径探测技术和基于地面平台的多径探测技术。这两类技术均已取得一定具有实际意义的研究成果。针对空中平台，该文围绕可行性验证、影响因素分析、建筑环境感知和非视距目标探测4个方面展开梳理；针对地面平台，该文则从目标检测与识别、目标二维定位、目标三维信息获取及新型探测方法4个方面展开论述。最后，对多径利用雷达目标探测技术进行总结和展望，指出该技术在目前实际应用中所面临的潜在问题和挑战。这些结果表明，多径利用雷达目标探测技术正朝着多样化、智能化的方向发展。

     

    Abstract: The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.

     

    • HTML全文

  • 图  1  典型L形NLOS场景

    Figure  1.  Typical L-shaped NLOS scenario

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    图  2  地面范围覆盖的概率与雷达高度的关系[6]

    Figure  2.  The probability of ground range coverage versus radar altitude[6]

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    图  3  MER雷达辅助地面动目标定位跟踪搭载平台及实验结果[16]

    Figure  3.  MER radar assisted ground moving target tracking platform and experimental results[16]

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    图  4  基于自适应波形设计的目标跟踪方法实验场景及实验结果[18]

    Figure  4.  Experimental scenario and results of target tracking method based on adaptive waveform design[18]

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    图  5  城市非视距环境中多个行人的微多普勒信号分析实验场景及实验结果[30]

    Figure  5.  Experimental scenario and results of micro-Doppler signal analysis of multiple pedestrians in urban NLOS environment[30]

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    图  6  非视距区域小型无人机微多普勒特征实验场景及实验结果[31]

    Figure  6.  Experimental scenario and results of micro-Doppler characteristics of small UAV in NLOS environment[31]

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    图  7  毫米波雷达基于衍射信号的人体识别实验场景及实验结果[34]

    Figure  7.  Human body recognition experiment scene and experimental results of millimeter wave radar based on diffraction signal[34]

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    图  8  基于多径鬼影空间位置匹配的非直视目标定位方法实验场景及实验结果[38]

    Figure  8.  Experimental scenes and results of NLOS target localization method based on multipath ghost spatial position matching[38]

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    图  9  基于双视角观测的单通道雷达非直视目标定位方法实验场景及实验结果[42]

    Figure  9.  Experimental scenario and results of NLOS target location detection based on dual-view observation with signal channel UWB radar[42]

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    图  10  基于网格匹配的非视距目标定位方法实验场景及实验结果[43]

    Figure  10.  Experimental scene and results of NLOS target localization method based on grid matching[43]

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    图  11  基于墙体位置估计的非视距目标探测方法实验场景及实验结果[44]

    Figure  11.  Experimental scenes and results of NLOS target detection method based on wall position estimation[44]

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    图  12  基于波束形成的数据域和图像域非视距目标成像算法仿真场景及仿真结果[48]

    Figure  12.  Simulation scene and simulation results of imaging algorithm for NLOS targets in data domain and image domain based on beamforming[48]

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    图  13  基于RMA的非视距动目标高精度成像方法实验场景及实验结果[52]

    Figure  13.  Experimental scene and result of high-precision imaging method for NLOS moving target based on range migration algorithm[52]

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    图  14  基于镜面对称反投影的非视距二维高精度成像算法实验场景及实验结果图[53]

    Figure  14.  Experimental scenes and results of NLOS 2D high-precision imaging algorithm based on MSBP[53]

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    图  15  X波段窄波束扫描雷达非视距目标定位仿真场景及仿真结果[55]

    Figure  15.  Simulation scene and simulation result of NLOS target localization for X-band narrow-beam scanning radar[55]

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    图  16  基于比相测角的非视距目标定位方法实验场景及实验结果[58]

    Figure  16.  Experimental scene and result of NLOS target localization method based on phase comparison angle measurement[58]

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    图  17  基于角度和距离的多径识别非视距目标定位方法实验场景及实验结果[62]

    Figure  17.  Experimental scene and result of multipath identification of NLOS target localization method based on angle and range[62]

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    图  18  非视距区域划分[66]

    Figure  18.  NLOS area division[66]

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    图  19  基于TOA匹配的目标定位方法实验结果[68]

    Figure  19.  Experimental result of target localization method based on TOA matching[68]

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    图  20  基于镜像目标定位方法实验结果[70]

    Figure  20.  Experimental result based on the mirror target localization method[70]

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    图  21  基于子空间匹配滤波器的遮蔽目标定位方法实验场景及实验结果[74]

    Figure  21.  Experimental scene and results of conceal target localization method based on subspace matching filter[74]

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    图  22  基于稀疏度驱动的建筑布局和目标位置联合估计方法实验场景及实验结果[78]

    Figure  22.  Experiment scene and joint estimation of NLOS building layout and target via sparsity-driven approach[78]

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    图  23  基于毫米波雷达的三维MSBP重建算法实验结果[80]

    Figure  23.  Experimental result of 3D MSBP reconstruction algorithm based on millimeter wave radar[80]

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    图  24  基于THz雷达的镜面折叠目标定位实验结果[82]

    Figure  24.  Experimental results of mirror folded target localization based on THz radar[82]

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    图  25  基于被动反射面的非视距车辆探测方法示意图[84]

    Figure  25.  The graph of NLOS vehicle detection method based on passive reflector[84]

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    图  26  基于RISs的非视距区域探测示意图[86]

    Figure  26.  NLOS region detection diagram based on RISs[86]

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    图  27  基于CNN的射频跟踪非直视目标定位方法实验场景及实验结果[89]

    Figure  27.  Experimental scene and result of the method of RF tracking NLOS target location based on CNN[89]

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    图  28  基于人工神经网络的多普勒雷达非视距目标定位跟踪方法原型车及实验结果[90]

    Figure  28.  Prototype vehicle and experimental results of Doppler radar NLOS target location and tracking method based on artificial neural network[90]

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    表  1  不同极化电磁波双程路径衰减随频率和材质变化[9] (dB)

    Table  1.   Two-way attenuation of different polarized EM waves varies with frequency and material[9] (dB)

    极化方式材质X波段Ku波段Ka波段
    垂直胶合板10～238～1913～38
    泥灰5～176～1810～25
    混凝土块10～225～205～20
    砖5～187～2410～25
    水平胶合板15～3315～4020～60
    泥灰3～1715～3017～50
    混凝土块10～3511～4312～57
    砖12～3516～4520～54
    下载: 导出CSV

    表  2  时变的特征向量在LOS和NLOS情况下对人体和圆柱体的分类结果[34]

    Table  2.   Classification results of human body and cylinder under LOS and NLOS by time-varying eigenvectors[34]

    示例准确度(%)SNR (dB)
    特征1
    (原始回波)    		特征2
    (时间倒数)    		特征3
    (时间偏移)    		特征4
    (STFT)    		人体圆柱体
    示例A(LOS)1009910010027.7335.43
    示例B(部分NLOS)10099100938.359.33
    示例C(全部NLOS)606483811.51–0.30
    下载: 导出CSV
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  • 收稿日期:  2023-07-25
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