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基于OMP算法的无人机微动多普勒特征检测与识别

Micro-Doppler Signature Detection and Recognition of UAVs Based on OMP Algorithm.

作者信息

Fan Shiqi, Wu Ziyan, Xu Wenqiang, Zhu Jiabao, Tu Gangyi

机构信息

Department of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.

出版信息

Sensors (Basel). 2023 Sep 15;23(18):7922. doi: 10.3390/s23187922.

DOI:10.3390/s23187922
PMID:37765981
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10535593/
Abstract

With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (OMP) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time-frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on OMP algorithm shows advantages in having a low signal-to-noise ratio (-10 dB). Field experiments indicate that this approach can effectively reduce clutter power (-15 dB) and successfully extract micro-Doppler signals for identifying different UAVs.

摘要

随着无人机在商业和军事领域的广泛应用，公众对无人机的识别和监管越来越关注。无人机的微多普勒特性能够反映其结构和运动信息，这为无人机识别提供了重要参考。无人机的低飞行高度和小雷达散射截面积（RCS）使得消除强地面杂波成为提取微弱微多普勒信号的关键问题。本文提出了一种基于正交匹配追踪（OMP）算法的杂波抑制方法，用于处理线性调频连续波（LFMCW）雷达获得的回波信号。该方法的重点在于稀疏表示思想，通过建立一套完整的环境杂波字典，有效抑制悬停无人机接收回波信号中的杂波。对处理后的信号进行时频域分析，根据无人机旋翼叶片的闪烁现象及相关微多普勒特性，估计未知无人机的特征参数。与传统信号处理方法相比，基于OMP算法的方法在低信噪比（-10 dB）情况下具有优势。现场实验表明，该方法能够有效降低杂波功率（-15 dB），并成功提取用于识别不同无人机的微多普勒信号。

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Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar.基于 77GHz 毫米波 FMCW 雷达的人体生命体征远程监测
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