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基于时频集中度指标的多旋翼无人机微动特征参数估计方法

宋晨 周良将 吴一戎 丁赤飚

引用本文: 宋晨, 周良将, 吴一戎, 丁赤飚. 基于时频集中度指标的多旋翼无人机微动特征参数估计方法[J]. 电子与信息学报, doi: 10.11999/JEIT190309 shu
Citation:  Chen SONG, Liangjiang ZHOU, Yirong WU, Chibiao DING. An Estimation Method of Micro-movement Parameters of UAV Based on The Concentration of Time-Frequency[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190309 shu

基于时频集中度指标的多旋翼无人机微动特征参数估计方法

    作者简介: 宋晨: 男,1992年生,博士生,研究方向为雷达信号处理、目标检测与识别等;
    周良将: 男,1981年生,研究员,研究方向为合成孔径雷达系统设计、系统误差补偿及其相关信号处理技术;
    吴一戎: 男,1963年生,研究员,中国科学院院士,研究方向为高分辨机载合成孔径雷达、SAR信号处理算法研究、遥感卫星地面处理与应用系统的体系结构等;
    丁赤飚: 男,1969年生,研究员,研究方向为先进合成孔径雷达系统和信号处理技术、数字信号处理等
    通讯作者: 周良将,ljzhou@mail.ie.ac.cn
摘要: 无人机旋翼转动产生的微多普勒调制能够反映此类目标的微动特性,准确估计无人机旋翼长度、转动频率对于无人机的检测与识别具有重要意义。该文针对调频连续波体制雷达,提出一种基于时频集中度指标(CTF)的多旋翼无人机微动特征参数估计方法,推导了无人机旋翼微动特征参数与微多普勒分量信号参数之间的映射关系,在时频旋转域基于时频集中度指标,提高了各微动分量的区分度,相比于传统方法,提高了多分量微多普勒信号的参数估计精度,在低信噪比环境下也具有很好的鲁棒性。通过仿真和实际场景实验验证了方法的有效性。

English

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  • 图 1  旋翼投影到雷达平面示意图

    图 2  多分量微多普勒信号参数估计方法流程图

    图 3  仿真数据时频旋转前后对比

    图 4  时频分析结和Hough变换仿真结果对比

    图 5  频谱集中度指标仿真结果

    图 6  不同方法微动参数估计误差对比

    图 7  实验数据处理结果

    表 1  微多普勒信号参数估计方法的计算效率对比

    方法STFT-HoughWVD-HoughHHTGWTCTF
    运算时间(s)89.420391.492464.842757.252671.2180
    下载: 导出CSV

    表 2  多次实验微动目标参数估计结果

    实验次数分量序号旋翼长度(cm)初始角度(°)旋翼转速(Hz)
    1112.5824.290.90
    212.5975.683.33
    2112.6148.5104.58
    212.63147.195.25
    3112.575.475.54
    212.55126.171.22
    下载: 导出CSV
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文章相关
  • 通讯作者:  周良将, ljzhou@mail.ie.ac.cn
  • 收稿日期:  2019-04-30
  • 录用日期:  2019-12-23
  • 网络出版日期:  2020-06-28
通讯作者: 陈斌, bchen63@163.com
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