摘要
随着网络化、智能化的发展,无人机(Unmanned aerial vehicles, UAVs)逐渐成为低空智联网(Low-altitude intelligent network, LAIN)的重要组成部分,但如何对低空智联网中的无人机平台进行有效的管理仍面临严峻挑战。基于无人机信号中的细微特征可对无人机进行个体识别,并检测是否为非法无人机,从而实现低空智联网中无人机的身份识别和管理。针对低空领域信道环境复杂且无法提前获取非法无人机信号样本的问题,本文提出了基于差值时频和多质心OpenMax的无人机开集识别方法。首先,提出了与信道无关的差值时频特征来降低多径信道环境对射频指纹(Radio frequency fingerprinting, RFF)特征的影响,并利用数据增强提高了识别模型的准确率和鲁棒性。其次,利用多质心OpenMax替代神经网络Softmax层,以实现无人机个体的开集识别。最后,对神经网络的损失函数进行了改进,提高了开集识别准确率。本文利用真实环境采集的数据对所提算法进行了验证,在多径信道环境中开放度为0.087时,开集识别准确率达到了93.23%,与基准算法相比,准确率分别提高了7.61%和13.4%。本文提出的算法可在复杂信道环境中有效识别无人机个体并检测出首次出现的非法无人机。
With the development of networked and intelligent unmanned aerial vehicles(UAVs),they have gradually become an important component of the low-altitude intelligent network(LAIN).However,the effective management of UAV platforms in the LAIN still faces severe challenges.Based on the subtle features of UAV signals,individual identification of UAVs can be achieved,and illegal UAVs can be detected,thereby realizing the identification and management of UAVs in the LAIN.In response to the problem of complex channel environments and the inability to obtain illegal UAV signal samples in advance in the low-altitude domain,this paper proposes an open set identification method for UAVs based on differential time-frequency and multi-center OpenMax.Firstly,this paper proposes channel-independent differential time-frequency features to reduce the impact of multipath channel environments on radio frequency fingerprinting(RFF)features and uses data augmentation to improve the accuracy and robustness of the identification model.Secondly,this paper uses multi-center OpenMax to replace the neural network’s SoftMax layer for open set identification of UAVs.Finally,the loss function of the neural network is improved to increase the accuracy of open set recognition.The proposed algorithm is validated using real-world data.When the openness is 0.087,the open set recognition accuracy reaches 93.23%,an increase of 7.61%and 13.4%compared with the benchmark algorithms.The algorithm proposed in this paper can effectively identify individual UAVs and detect illegal UAVs appearing for the first time in complex channel environments.
作者
杨宁
胡景明
张邦宁
丁国如
郭道省
YANG Ning;HU Jingming;ZHANG Bangning;DING Guoru;GUO Daoxing(College of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China)
出处
《数据采集与处理》
CSCD
北大核心
2024年第1期60-70,共11页
Journal of Data Acquisition and Processing
关键词
低空智联网
开集识别
射频指纹
多径信道环境
非法无人机
low-altitude aerial intelligent network
open set identification
radio frequency fingerprinting
complex channel environment
illegal UAVs
作者简介
杨宁(1996⁃),女,博士研究生,研究方向:辐射源个体识别、物理层安全,E⁃mail:corrine_yang@126.com;胡景明(1978⁃),男,副教授,研究方向:卫星通信、信号处理等;张邦宁(1963⁃),男,教授,博士生导师,研究方向:卫星通信、通信抗干扰技术、辐射源个体识别等;丁国如(1986⁃),男,教授,博士生导师,研究方向:数字孪生、信号处理、电磁频谱感知等;通信作者:郭道省(1973⁃),男,教授,博士生导师,研究方向:辐射源个体识别、卫星通信、通信抗干扰技术等,E⁃mail:xyzgfg@sina.com。
