多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)及高分辨微动特征提取方法

陈小龙 袁旺 杜晓林 于刚 何肖阳 关键 汪兴海

陈小龙, 袁旺, 杜晓林, 等. 多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)及高分辨微动特征提取方法[J]. 雷达学报(中英文), 2024, 13(3): 539–553. doi: 10.12000/JR23142
引用本文: 陈小龙, 袁旺, 杜晓林, 等. 多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)及高分辨微动特征提取方法[J]. 雷达学报(中英文), 2024, 13(3): 539–553. doi: 10.12000/JR23142
CHEN Xiaolong, Yuan Wang, Du Xiaolin, et al. Multiband FMCW radar LSS-target detection dataset (LSS-FMCWR-1.0) and high-resolution micromotion feature extraction method[J]. Journal of Radars, 2024, 13(3): 539–553. doi: 10.12000/JR23142
Citation: CHEN Xiaolong, Yuan Wang, Du Xiaolin, et al. Multiband FMCW radar LSS-target detection dataset (LSS-FMCWR-1.0) and high-resolution micromotion feature extraction method[J]. Journal of Radars, 2024, 13(3): 539–553. doi: 10.12000/JR23142

多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)及高分辨微动特征提取方法

doi: 10.12000/JR23142
基金项目: 国家自然科学基金(62222120, 61931021),山东省自然科学基金(ZR201YQ43)
详细信息
    作者简介:

    陈小龙,博士,教授,主要研究方向为雷达低慢小目标检测、海杂波抑制、雷达智能信号处理等

    袁 旺,硕士生,主要研究方向为高分辨雷达无人机等低慢小目标多特征融合识别

    杜晓林,博士,副教授,硕士生导师,主要研究方向为雷达信号处理、波形设计、协方差矩阵估计等

    于 刚,博士,副教授,主要研究方向为非线性信号处理、时频分析算法等

    何肖阳,硕士生,主要研究方向为海杂波背景下的目标检测

    关 键,博士,教授,博士生导师,主要研究方向为雷达目标检测与跟踪、侦查图像处理和信息融合

    汪兴海,硕士,副教授,主要研究方向为雷达系统设计、数字信号处理等

    通讯作者:

    陈小龙 cxlcxl1209@163.com

    关键 guanjian_68@163.com

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

Multiband FMCW Radar LSS-target Detection Dataset (LSS-FMCWR-1.0) and High-resolution Micromotion Feature Extraction Method

Funds: The National Natural Science Foundation of China (62222120, 61931021), The Natural Science Foundation of Shandong (ZR201YQ43)
More Information
  • 摘要: 无人机等低慢小目标探测对雷达目标检测和识别技术提出了很高的挑战,迫切需要构建相关数据集,支撑低慢小探测技术的发展和应用。该文公开了一个多波段调频连续波(FMCW)雷达低慢小目标探测数据集,基于Ku波段和L波段的FMCW雷达采集6种类型的无人机回波数据,通过雷达调制周期和调制带宽,具备不同时域和频域分辨和测量能力,构建了多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)。为了进一步提升无人机微动特征提取能力,该文提出基于局部极大值同步提取变换的无人机微动提取和参数估计方法,在短时傅里叶变换的基础上提取时频能量最大值,保留有用信号分量,实现精细化时频表示。基于LSS-FMCWR-1.0进行验证分析,结果表明该方法相较于传统时频方法,熵值平均降低了5.3 dB,旋翼叶长估计误差降低了27.7%,所提方法兼顾高时频分辨率和较高的参数估计精度,为后续目标识别奠定了基础。

     

  • 图  1  FMCW雷达组成

    Figure  1.  Component of the FMCW radar

    图  2  Ku波段和L波段FMCW

    Figure  2.  Ku-band and L-band FMCW radar

    图  3  三角波调制FMCW雷达发射和接收信号示意图

    Figure  3.  Diagram of the transmitted and received signals of the triangular wave modulated FMCW radar

    图  4  回波信号处理流程

    Figure  4.  Echo signal processing process

    图  5  各类型无人机时频谱图及距离周期图

    Figure  5.  Time spectrum and range Doppler of various types of unmanned aerial vehicles

    图  6  固定翼无人机的时频图(06-2023.5.8-0.3-100-9-K (1))

    Figure  6.  Time spectrum diagram of fixed wing UAV

    图  7  无人机采集场景以及无人机类型

    Figure  7.  UAV acquisition scenarios and types of UAVs

    图  8  具体数据集结构示意图

    Figure  8.  Schematic diagram of specific dataset structure

    图  9  算法流程图

    Figure  9.  Algorithm flow chart

    图  10  回波预处理流程

    Figure  10.  Echo data preprocessing process

    图  11  去抖动干扰前和去抖动干扰后的距离周期图(LSS-FMCWR-1.0:06-2023.5.8-0.3-100-9-K (1).mat)

    Figure  11.  Data before and after removing jitter interference (LSS-FMCWR-1.0:06-2023.5.8-0.3-100-9-K (1).mat)

    图  12  不同时频方法的Renyi熵值

    Figure  12.  Renyi entropy values for different time-frequency methods

    图  13  在不同信噪比下,不同时频方法的Renyi熵值

    Figure  13.  Under different SNRs, the Renyi entropies of the different time-frequency analysis methods

    图  14  悟2无人机信号STFT和LSET (LSS-FMCWR-1.0: 04-2023.5.8-0.3-100-13-L (18).mat)

    Figure  14.  STFT and LSET of Wu 2 UAV signal (LSS-FMCWR-1.0: 04-2023.5.8-0.3-100-13-L (18).mat)

    图  15  悟2无人机旋翼时域信号(04)

    Figure  15.  Time domain signal of Wu 2 UAV (04)

    图  17  LSS-FMCWR-1.0:多波段FMCW雷达低慢小探测数据集

    Figure  17.  Release webpage of LSS-FMCWR-1.0: Multiband FMCW radar LSS-target echo dataset

    表  1  Ku波段和L波段FMCW雷达主要技术参数

    Table  1.   Ku-band and L-band FMCW radar main technical indicators

    FMCW雷达
    波段
    工作频率(GHz) 调制带宽(MHz) 调制周期
    (ms)
    采样频率(kHz)
    Ku波段 23.7 10~2000 0.200~10.000 500
    L波段 1.4~1.5 100 0.300~8.192 500
    下载: 导出CSV

    表  2  无人机主要技术参数

    Table  2.   The main technical parameters of the UAV

    无人机类型 旋翼个数 转速(r/min) 叶片长度(cm)
    大疆御2 4 1950 11
    大疆精灵 4 1320 13
    大疆M350 4 1750 11
    大疆悟2 4 1500 19
    大疆M600 6 1620 12
    固定翼无人机 1 360 12
    下载: 导出CSV

    表  3  无人机采集参数设置(目标距离为估值)

    Table  3.   UAV acquisition parameter settings

    无人机类型及编号 调制带宽(MHz) 调制周期(ms) 目标距离(m) 雷达波段
    Ku/L
    大疆御2(01) 100 0.300 9 Ku+L
    1.024 9 Ku+L
    大疆精灵(02) 100 0.300 9 Ku+L
    1.024 9 Ku+L
    大疆M350(03) 100 0.300 12 Ku+L
    1.024 12 Ku+L
    200 0.300 12 Ku
    1.024 12 Ku
    300 0.300 12 Ku
    1.024 12 Ku
    大疆悟2(04) 100 0.300 11 Ku+L
    1.024 11 Ku+L
    大疆M600(05) 100 0.300 8 Ku
    11 L
    1.024 11 Ku
    13 L
    固定翼无人机(06) 100(正视) 0.300(低速) 9 Ku+L
    0.300(高速) 9 Ku+L
    4.192 9 Ku+L
    500 4.192 9 Ku
    100(侧视) 0.300(低速) 9 Ku+L
    0.300(高速) 9 Ku+L
    4.192 9 Ku+L
    500 4.192 9 Ku
    下载: 导出CSV

    表  4  时频方法的运算时间

    Table  4.   Time-frequency method execution time

    时频方法运行时间(s)
    STFT0.019
    SST1.053
    SET0.063
    LSET0.503
    下载: 导出CSV

    表  5  悟2无人机叶片转速和长度的估计

    Table  5.   Estimation of blade speed and length for Wu2 UAV

    时频分析方法 转速(r/s) 叶片长度(cm)
    理论值 估计值 相对误差 理论值 估计值 相对误差
    STFT 25 22.83 8% 19 25.6 34.7%
    LSET 25 23.4 6% 19 20.5 7.0%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-08-19
  • 修回日期:  2023-10-15
  • 网络出版日期:  2023-11-14
  • 刊出日期:  2024-06-28

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