飞鸟与无人机目标雷达探测与识别技术进展与展望

陈小龙 陈唯实 饶云华 黄勇 关键 董云龙

陈小龙, 陈唯实, 饶云华, 等. 飞鸟与无人机目标雷达探测与识别技术进展与展望[J]. 雷达学报, 2020, 9(5): 803–827. doi: 10.12000/JR20068
引用本文: 陈小龙, 陈唯实, 饶云华, 等. 飞鸟与无人机目标雷达探测与识别技术进展与展望[J]. 雷达学报, 2020, 9(5): 803–827. doi: 10.12000/JR20068
CHEN Xiaolong, CHEN Weishi, RAO Yunhua, et al. Progress and prospects of radar target detection and recognition technology for flying birds and unmanned aerial vehicles[J]. Journal of Radars, 2020, 9(5): 803–827. doi: 10.12000/JR20068
Citation: CHEN Xiaolong, CHEN Weishi, RAO Yunhua, et al. Progress and prospects of radar target detection and recognition technology for flying birds and unmanned aerial vehicles[J]. Journal of Radars, 2020, 9(5): 803–827. doi: 10.12000/JR20068

飞鸟与无人机目标雷达探测与识别技术进展与展望

doi: 10.12000/JR20068
基金项目: 国家自然科学基金(U1933135, 61871391, 61931021),山东省重点研发计划(2019GSF111004),基础加强计划技术领域基金(2102024),装发“十三五”领域基金(61404130212)
详细信息
    作者简介:

    陈小龙(1985–),男,山东烟台人,博士,副教授。主要研究方向为雷达动目标检测、海杂波抑制、雷达信号精细化处理等。获中国专利优秀奖、军队科技进步一等奖、中国产学研合作促进会军民融合奖、中国电子学会/全军优博。入选中国科协青托,第七届中国电子学会优秀科技工作者,世界无线电联盟、国际应用计算电磁学会青年科学家奖。E-mail: cxlcxl1209@163.com

    陈唯实(1982–),男,天津人,博士,研究员。主要研究方向为机场运行安全管理、无人机反制、鸟击防范、低空空域监视等。E-mail: chenwsh@mail.castc.org.cn

    饶云华(1972–),男,博士,副教授,主要研究方向为新体制雷达,雷达系统设计与信号处理等。E-mail: ryh@whu.edu.cn

    黄 勇(1979–),男,副教授,主要研究方向为雷达目标检测、多维信号处理等。E-mail: huangyong_2003@163.com

    关 键(1968–),男,辽宁锦州人,教授,博士生导师。主要研究方向为雷达目标检测与跟踪、侦察图像处理和信息融合。E-mail: guanjian_68@163.com

    董云龙(1974–),男,天津宝坻人,教授,主要研究方向为多传感器信息融合。E-mail: china_dyl@sina.com

    通讯作者:

    陈小龙 cxlcxl1209@163.com

  • 责任主编:张群 Corresponding Editor: ZHANG Qun
  • 中图分类号: TN957.51

Progress and Prospects of Radar Target Detection and Recognition Technology for Flying Birds and Unmanned Aerial Vehicles (in English)

Funds: The National Natural Science Foundation of China (NSFC) (U1933135, 61871391, 61931021), Key Research and Development Program of Shandong (2019GSF111004), Fundamental Strengthening Technology Program (2102024), Foundation of the Equipment development of the “13th Five-Year Plan” (61404130212)
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  • 摘要: 飞鸟和无人机(UAVs)是典型的“低慢小”目标,具有低可观测性,对两者的有效监视和识别成为保障空中航路安全、城市安保等需求迫切需要解决的难题。飞鸟和无人机目标类型多、飞行高度低、机动性强、雷达散射截面积小,加之探测环境复杂,给目标探测带来极大困扰,已成为世界性难题。因此迫切需要研发“看得见(检测能力强)、辨得明(识别概率高)”的无人机、飞鸟等“低慢小”目标监视手段和技术,实现目标的精细化描述和识别。该文集中对近年来复杂场景下旋翼无人机和飞鸟目标检测与识别技术的研究进展进行了归纳总结,介绍了飞鸟和无人机探测的主要手段,从回波建模和微动特性认知、泛探模式下机动特征增强与提取、分布式多视角特征融合、运动轨迹差异、深度学习智能分类等方面给出了检测和识别的有效途径。最后,该文总结了现有研究存在的问题,对未来复杂场景下飞鸟和无人机目标检测与识别技术的发展进行了展望。

     

  • 图  1  飞鸟、无人机对民航飞机的威胁

    Figure  1.  The threat of flying birds and UAVs to civil aviation aircraft

    图  2  无人机危害公共安全

    Figure  2.  UAVs endanger public safety

    图  3  “低慢小”目标雷达回波

    Figure  3.  “Low, slow, small” target radar echo

    图  4  典型探鸟雷达系统

    Figure  4.  Typical avian radar systems

    图  5  典型非合作无人机探测系统

    Figure  5.  Typical non-cooperative UAV detection system

    图  6  国际典型的“低慢小”目标探测系统

    Figure  6.  International typical “low, slow, small” target detection system

    图  7  DVB-T外辐射源雷达AULOS无人机探测结果[35]

    Figure  7.  DVB-T passive radar AULOS UAV detection results[35]

    图  8  基于DTMB外辐射源雷达的无人机探测实验[38]

    Figure  8.  UAV detection experiment based on DTMB passive radar[38]

    图  9  基于数字电视信号外辐射源雷达的无人机目标监视应用场景示例

    Figure  9.  UAV target surveillance application based on digital TV signal passive radar

    图  10  德国FHR多通道无源雷达系统GAMMA-2[42]

    Figure  10.  German FHR multi-channel passive radar system GAMMA-2[42]

    图  11  英国伦敦大学NetRAD雷达系统[44]

    Figure  11.  NetRAD radar system of university of London[44]

    图  12  荷兰Robin飞鸟/无人机识别雷达[45]

    Figure  12.  The Robin birds/UAV recognition radar in Netherlands[45]

    图  13  英国Aveillant公司全息雷达系统[46]

    Figure  13.  The holographic radar system from Aveillant, UK[46]

    图  14  旋翼无人机微动特性[53]

    Figure  14.  Micro-motion characteristics of six-rotor UAV[53]

    图  15  飞鸟与无人机微动特征差异

    Figure  15.  Differences in micromotion characteristics of flying birds and UAVs

    图  16  仿真的飞鸟目标雷达微动特性分析

    Figure  16.  Analysis of radar m-D characteristics of simulated flying bird

    图  17  无人机目标实测微动特性分析(外辐射源中心频率658 MHz)[39]

    Figure  17.  Analysis of measured m-D characteristics of UAV target (center frequency of external radiation source 658 MHz)[39]

    图  18  无人机和飞鸟目标微多普勒(24 GHz)[54]

    Figure  18.  Micro-Doppler (24 GHz) for UAV and bird targets[54]

    图  19  双基地外辐射源雷达配置图

    Figure  19.  Configuration diagram of bistatic passive radar

    图  20  非参数搜索长时间相参积累(NUSP-LTCI)处理流程[58]

    Figure  20.  NUSP-LTCI processing[58]

    图  21  低空飞行目标雷达相参积累结果对比分析[58,60]

    Figure  21.  Comparative analysis of radar coherent integration results of low-altitude flying targets[58,60]

    图  22  单频网分布式外辐射源雷达微动特征提取研究思路

    Figure  22.  Method of micro-motion feature extraction via single-frequency distributed passive radar

    图  23  CNN中各卷积层的数据特征(以LeNet为例)[73]

    Figure  23.  Data characteristics of convolutional layers in CNN (take LeNet as an example)[73]

    图  24  基于深度学习的微动参数估计流程示意图

    Figure  24.  Schematic diagram of m-D parameter estimation based on deep learning

    图  25  基于雷达数据的飞鸟与无人机目标识别算法流程

    Figure  25.  Target recognition algorithm of flying birds and UAVs based on radar data processing

    图  26  飞鸟与无人机目标飞行轨迹识别结果[77]

    Figure  26.  Recognition results of trajectories of flying birds and UAVs[77]

    图  27  某机场鸟类活动热点统计情况[79]

    Figure  27.  Statistics of hot spots of birds activity at an airport[79]

    图  28  无人机和飞鸟目标外场探测试验

    Figure  28.  UAV and flying birds detection experiment

    图  1  The threat of flying birds and UAVs to civil aviation aircraft

    图  2  UAVs endanger public safety

    图  3  “low, slow and small” target radar echo

    图  4  Typical avian radar systems

    图  5  Typical noncooperative UAV detection system

    图  6  International typical “low, slow and small” target detection system

    图  7  DVB-T passive radar AULOS UAV detection results[35]

    图  8  UAV detection experiment based on DTMB passive radar[38]

    图  9  UAV target surveillance application based on digital TV signal passive radar

    图  10  German FHR multi-channel passive radar system GAMMA-2[42]

    图  11  NetRAD radar system of university of London[44]

    图  12  The Robin birds/UAV recognition radar in Netherlands[45]

    图  13  The holographic radar system from Aveillant, UK[46]

    图  14  Micromotion characteristics of six-rotor UAV[53]

    图  15  Differences in micromotion characteristics of flying birds and UAVs

    图  16  Analysis of radar m-D characteristics of simulated flying bird

    图  17  Analysis of measured m-D characteristics of UAV target (center frequency of external radiation source 658 MHz)[39]

    图  18  Micro-Doppler (24 GHz) for UAV and bird targets[54]

    图  19  Configuration diagram of bistatic passive radar

    图  20  NUSP-LTCI processing[58]

    图  21  Comparative analysis of radar coherent integration results of low-altitude flying targets[58,60]

    图  22  Method of micromotion feature extraction via single-frequency distributed passive radar

    图  23  Data characteristics of convolutional layers in CNN (take LeNet as an example)[73]

    图  24  Diagram of m-D parameter estimation based on deep learning

    图  25  Target recognition algorithm of flying birds and UAVs based on radar data processing

    图  26  Recognition results of trajectories of flying birds and UAVs[77]

    图  27  Statistics of hot spots of birds activity at an airport[79]

    图  28  UAV and flying birds detection experiment

    表  1  国外3种典型探鸟雷达产品说明

    Table  1.   Description of three typical foreign avian radar products

    产品名称 技术特点 部署方式
    Merlin 水平和垂直扫描雷达结合,固态发射机 水平扫描雷达通常部署在靠近机场中心的位置,负责机场周边低空预警;垂直扫描雷达通常部署在每条跑道中心一侧,负责监视航班起降通道;当然,雷达部署是个复杂的系统问题,需要考虑净空障碍物限制面、地物遮挡、供电等多方面因素。
    Accipiter 水平和垂直扫描雷达结合,附加抛物面天线
    Robin 水平和垂直扫描雷达结合,磁控管发射机
    下载: 导出CSV

    表  2  鸟类热点月度轨迹数量统计

    Table  2.   Monthly statistics of bird hot spots

    热点编号 轨迹数量 百分比(%)
    1 7628 0.73
    2 30299 2.89
    3 22484 2.15
    4 16885 1.61
    5 21987 2.10
    6 15652 1.50
    7 29856 2.85
    8 33938 3.24
    9 21650 2.07
    10 846406 80.86
    下载: 导出CSV

    表  1  Description of three typical foreign avian radar products

    Product name Merlin Accipiter Robin
    Technical characteristics Technical features combination of hori-zontal and vertical scanning radar,
    solid state transmitter
    Combination of horizontal and vertical scanning radar, additional parabolic antenna additional parabolic antenna combination of horizontal and vertical scanning radar, magn-etron transmitter
    Deployment method Horizontal scanning radars are usually deployed close to the center of the airport and are responsible for low-altitude warnings around the airport; vertical scanning radars are usually deployed on the center side of each runway and are responsible for monitoring flight take-off and landing channels; of course, radar deployment is a complex system problem that needs to be considered, there are many factors such as clearance obstacle restriction surface, ground obstruction, power supply and so on.
    下载: 导出CSV

    表  2  Monthly statistics of bird hot spots

    Hot number Track number The percentage (%)
    1 7628 0.73
    2 30299 2.89
    3 22484 2.15
    4 16885 1.61
    5 21987 2.10
    6 15652 1.50
    7 29856 2.85
    8 33938 3.24
    9 21650 2.07
    10 846406 80.86
    下载: 导出CSV
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  • 收稿日期:  2020-05-27
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