低频电磁波建筑物内部结构透视技术研究进展

金添 宋勇平 崔国龙 郭世盛

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
引用本文: 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
Citation: 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

低频电磁波建筑物内部结构透视技术研究进展

doi: 10.12000/JR20119
基金项目: 国家自然科学基金(61971430)
详细信息
    作者简介:

    金添:金 添(1980–),男,国防科学技术大学教授,博士生导师,研究方向为超宽带雷达成像、隐蔽目标检测识别等。全国百篇优秀博士论文获得者,入选教育部“新世纪优秀人才支持计划”,获国际无线电科学联盟青年科学家奖,评为中国电子学会优秀科技工作者。“信号处理与系统”国家精品课程和资源共享课主讲教师,信号处理系列课程国家级教学团队主要成员。已发表论文100余篇,获授权国家发明专利7项,出版专著3部、译著1部、教材1部。《雷达学报》、《信号处理》、《雷达科学与技术》等期刊编委

    宋勇平(1989–),男,空军预警学院雷达士官学校讲师,博士,研究方向为穿墙探测、MIMO雷达成像、微弱目标检测

    崔国龙(1982–),男,电子科技大学教授,青年长江学者,博士生导师,《雷达学报》编委。研究方向为最优化理论和算法、雷达目标检测理论、波形多样性以及城市环境目标探测等

    郭世盛(1991–),男,电子科技大学特聘副研究员,硕士生导师,研究方向为城市环境遮蔽目标探测、基于雷达的人体行为识别

    通讯作者:

    金添 tianjin@nudt.edu.cn

  • 责任主编:孔令讲 Corresponding Editor: KONG Lingjiang
  • 中图分类号: TN957

Advances on Penetrating Imaging of Building Layout Technique Using Low Frequency Radio Waves

Funds: The National Natural Science Foundation of China (61971430)
More Information
  • 摘要: 在进入陌生建筑物内部之前获取其内部结构信息,能够为反恐作战、灾害救援等多种应用提供服务,具有重要的现实意义和研究价值。低频电磁波能够穿透常见建筑物材料传播,进而安全、稳定、隐蔽地获取墙后目标信息。利用低频电磁波获取墙后信息因此成为建筑物内部结构透视领域的研究重点。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,利用低频电磁波进行建筑物内部结构穿透探测的技术目前主要包括3类:基于反射波测量的穿墙雷达成像技术、基于透射波测量的射频层析成像技术、基于多径信号的墙体位置估计技术。这3类技术均已取得一定具有实际意义的研究成果。该文围绕这3类技术所涵盖主要内容的发展历程进行了梳理,主要包括穿墙雷达墙后静止目标成像原理、基于穿墙雷达的建筑物内部结构观测模式、基于穿墙雷达成像的建筑物内部结构重建技术、基于射频层析成像的建筑物内部结构反演技术、基于多径信号的墙体位置估计技术,并以此对该领域的发展趋势进行了探讨。总结近20年以来低频电磁波建筑物内部结构透视技术的发展历程,可以发现建筑物内部结构穿透探测平台已由传统的机载、车载平台转向微型机器人、无人机等新型平台,而对应的建筑物内部结构信息重建方法,则由传统的雷达成像技术,发展成包含图像增强、稀疏重构等在内的多种新型方法。这些结果表明,建筑物内部结构透视技术正朝着系统化、多样化、智能化的方向发展。

     

  • 图  1  穿墙探测的一般信号传播模型

    Figure  1.  General signal propagation model for through-the-wall detection

    图  2  SIRE在多视角探测模式下的实测数据成像结果

    Figure  2.  Imaging results of SIRE’s measured data in multi-view detection mode

    图  3  单边双点观测模式及其实验结果[18]

    Figure  3.  Single-side two-location mode and its experiment result[18]

    图  4  SAPPHIRE雷达系统及其实验结果[19,20]

    Figure  4.  SAPPHIRE radar system and its experiment result[19,20]

    图  5  小型移动平台上的穿墙雷达系统及其实验结果[23]

    Figure  5.  Through-the-wall radar system mounted on a small mobile platform and its experiment result[23]

    图  6  全方向穿墙探测[28]

    Figure  6.  All-dricetions through-wall detection[28]

    图  7  多系统结合的穿墙探测示意图[31,32]

    Figure  7.  Schematic of multi-system combined through-wall detection[31,32]

    图  8  利用Hough变换提取墙体的直线特征[35]

    Figure  8.  Using Hough transformation to extract the straight-line features of the wall[35]

    图  9  利用M-N-K检测器提取建筑物结构[38]

    Figure  9.  Using M-N-K detector to extract building structure[38]

    图  10  基于多方位多尺度的建筑布局成像融合方法[40]

    Figure  10.  Multi-azimuth and multi-scale building layout imaging fusion method[40]

    图  11  原始模糊的建筑物结构图像与生成重建的建筑物结构图像[41]

    Figure  11.  Original blurred building structure images and generated building structure images[41]

    图  12  基于MST的建筑物内部结构估计[43]

    Figure  12.  Estimation of building internal structure based on MST[43]

    图  13  直线结构辅助下的墙体稀疏重构[46]

    Figure  13.  Sparse reconstruction of wall with the aid of linear structure[46]

    图  14  CF加权下的建筑物结构稀疏成像[47]

    Figure  14.  Sparse imaging of building structure under CF weighting[47]

    图  15  基于TV正则化的建筑物结构稀疏重构[48]

    Figure  15.  Sparse reconstruction of building structure based on TV regularization[48]

    图  16  基于WiFi信号的机器人平台穿墙探测及场景反演结果[57]

    Figure  16.  WiFi signal-based robot platform through-the-wall detection and scene inversion results[57]

    图  17  无人机载平台的WiFi探测实验与场景反演结果[59]

    Figure  17.  Experiments on Unmanned Aerial Vehicles (UAVs) and scene inversion results[59]

    图  18  基于RETINA的RTI算法[60]

    Figure  18.  RTI algorithm based on RETINA[60]

    图  19  基于椭圆协方差约束与叠加像素衰减非负约束的RTI结果对比[62]

    Figure  19.  Comparison of RTI results based on elliptic shape and superimposed pixel attenuation non-negative constraints[62]

    图  20  基于TV约束与PIC-ART的RTI结果对比[64]

    Figure  20.  Comparison of RTI results by TV constraint and by PIC-ART[64]

    图  21  基于传播时延的建筑结构层析成像[66]

    Figure  21.  Building structure tomography based on propagation time delay[66]

    图  22  基于内部辐射源的建筑物结构全息成像[67]

    Figure  22.  Holographic imaging of building structure based on internal radiation source[67]

    图  23  基于多径信号的目标定位与建筑布局联合估计[9]

    Figure  23.  Joint target location and building layout estimation based on multipath signals[9]

    图  24  利用目标运动轨迹反推墙体位置[68]

    Figure  24.  Use trajectory to infer wall position[68]

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出版历程
  • 收稿日期:  2020-08-27
  • 修回日期:  2020-10-19
  • 网络出版日期:  2020-11-02
  • 刊出日期:  2021-06-28

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