基于遥控信号频谱特征的无人机识别算法
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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.
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.
引文
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[2]沈建潮,朱辉.基于无人机的无线电监测测向系统校准方法研究[J].中国无线电,2018(1):43-45.
[3]于宜.系留多旋翼无人机在无线电监测中的应用[J].中国无线电,2018(1):39-40.
[4]李笔勇,孙晔,朱辉,等.无线电固定监测设施测试验证方法初探[J].中国无线电,2018(1):53-55.
[5]美军获准摧毁非法靠近军事基地的无人机[J].保密工作,2017(9):57.
[6]丁百卉.无人机反制技术在无线电监测中的应用[J].中国新通信,2017,19(8):90.
[7]贺思捷,刘大同,彭宇.遥测数据驱动的无人机飞行状态识别方法[J].仪器仪表学报,2016,37(9):2004-2013.
[8]武枞,王伟.武警部队在执勤中对非法小型无人机防范问题探究[J].国防科技,2016,37(4):99-103.
[9]杨伟强,方鼎.多旋翼无人机在无线电监测中的应用[J].中国无线电,2015(12):28-30.
[10]蒋兆军,成孝刚,彭雅琴,等.基于深度学习的无人机识别算法研究[J].电子技术应用,2017,43(7):84-87.
[11]息朝健,郭三学.基于简化Forstner算子改进的SIFT无人机图像识别算法[J].计算机应用与软件,2012,29(5):254-255.
[12]费连.利用无人驾驶空基平台进行无线电监测的设想[J].中国无线电,2008(12):47-52.
[13]田垅,刘宗田.最小二乘法分段直线拟合[J].计算机科学,2012,39(S1):482-484.
[14]杜玉龙,李建增,张岩,等.基于强独特性二进制描述子的无人机目标识别算法[J].电子技术应用,2016,42(10):24-28.
[15]谭颖,刘技恒.远程采集无人机图像的特征识别方法研究[J].计算机测量与控制,2015,23(3):1005-1007.
[16]闫昊昱,刘佳琳,辛亮亮.基于计算机图像识别与GPS的无人机导航系统设计[J].电子制作,2015(13):74-75.
[17]史阳阳,庄丽葵,曹云峰,等.基于DSP的无人机自主着舰识别算法[J].太赫兹科学与电子信息学报,2013,11(5):712-717.
[18]聂振钢,张晓林.一种新的无人机监控图像实时目标识别算法[J].遥测遥控,2007(2):1-4.
[19]崔跃,李春涛.无人机障碍探测与识别技术研究[J].计算机测量与控制,2007(3):294-296.
[20] XU Guili,ZHANG Yong,JI Shengyu,et al. Research on Computer Vision-based for UAV Autonomous Landing on a Ship[J]. Pattern Recognition Letters,2008,30(6):6 00-605.
[21] WANG Xufeng,KONG Xingwei,ZHI Jianhui,et al.Realtime Drogue Recognition and 3D Locating for UAV Autonomous Aerial Refueling Based on Monocular Machine Vision[J].Chinese Journal of Aeronautics,2015,28(6):1667-1675.