无人机(unmanned aerial vehicle,UAV)行业的快速发展给重要场所的低空空域带来安全隐患.为了对无人机实施有效管制,研制一套能够识别无人机信号的无线电侦测系统有重要意义.针对相似无人机之间识别困难的问题,提出了一种基于图控分离和2次分类的无人机信号识别方法.该方法基于无人机图像传输信号(image transmission signal,ITS)的循环特性提取其时域参数,采用分类决策树对无人机进行初步分类识别;再通过分离无人机的图像传输信号与飞行控制信号(flight control signal,FCS)的方式分别提取其时频特征参数;最后进行了 2次分类识别.实验结果表明,对于6种常见无人机的通信信号,在信噪比(signal-to-noise ratio,SNR)为0 dB时平均识别准确率可达97.4％,说明该方法可以精确识别无人机.
UAV signal recognition method based on re-classification and separation of image transmission signal and flight control signal
The rapid development of the unmanned aerial vehicle(UAV)industry has introduced security risks in low-altitude airspaces.To effectively control UAVs,a radio detection system that can identify UAV signals should be developed.To identify similar UAVs,this study proposes a UAV signal recognition method based on reclassification and separation of image transmission signals(ITSs)and flight control signals(FCSs).The pro-posed method extracts its time-domain parameters using the cyclic characteristics of the ITS and applies a classification decision tree to initially classify and identify the UAVs.Then,by separating the ITS and FCS,their time-frequency characteristic parameters are extracted,and finally,secondary classification is performed.Experimental results show that for the communication signals of six common UAVs,the average recognition accuracy can reach 97.4％when the signal-to-noise ratio is 0 dB,indicating that the method can accurately identify UAVs.
radio detectionorthogonal frequency division multiplexing(OFDM)seg-mented fast Fourier transform(FFT)time frequency analysisfeature extraction
万方数据
维普
