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.

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