摘要
近年来,无人机"黑飞"事件与非法入侵的案例开始激增,现有的识别技术往往难以实时、准确地检测到非法无人机,为此提出了一种基于遥控信号频谱特征的无人机识别算法。该算法通过背景频谱模板学习(利用二次平均的方法求信号检测阈值,并屏蔽宽带信号对无人机遥控信号检测的干扰)、无人机跳频与定频遥控信号检测、机型判断及其特征参数学习三大模块来识别非法入侵的无人机。实验结果表明,该算法可以实现对常见无人机实时准确地识别,且鲁棒性较强。
In recent years,the cases of“black flying”and illegal invasion of UAVs have been increasing rapidly.It is often difficult for existing identification techniques to detect UAVs accurately in real time.A UAV recognition algorithm based on the spectrum characteristics of remote control signals is proposed.The algorithm uses three modules to identify the illegal invading UAVs,which include background spectrum template learning(using the double-averaging method to obtain signal detection threshold,and shielding the interference of wideband signal to UAV remote control signal detection),UAV frequency hopping and fixed frequency remote control signal detection,and model judgment and its characteristic parameters learning.Many experiments show that the algorithm can realize real-time and accurate recognition of common UAVs and has strong robustness.
作者
陈君胜
杨小勇
徐怡杭
CHEN Junsheng;YANG Xiaoyong;XU Yihang(Radio Monitoring Station In Gansu,Lanzhou 730000,China;National Institute for Radio Spectrum Management,Xi’an 710061,China;School of Electronic and Information in Northwestern Polytechnical University,Xi’an 710072,China)
出处
《无线电工程》
2019年第2期101-106,共6页
Radio Engineering
基金
国家自然科学基金资助项目(61203233)
关键词
无人机识别
频谱模板
跳频信号
特征参数学习
UAV recognition
spectrum template
frequency hopping signal
characteristic parameters learning
作者简介
陈君胜,男,(1961—),毕业于兰州大学计算机应用专业,高级工程师。主要研究方向:无线电监测和无线电设备检测;杨小勇,男,(1975—),硕士,高级工程师。主要研究方向:无线电监测;徐怡杭,男,(1997—),西北工业大学电子信息工程专业,本科生。
