一种无人机辐射源调制样式识别算法  

A Modulation Recognition Algorithm for UAV Emitter

作　　者：黄祥 徐静 都晨[1] 郭昭艺 吴涛 HUANG Xiang;XU Jing;DU Chen;GUO Zhao-yi;WU Tao(Jiangsu Frontier Electric Power Technology CO.,LTD,Jiangsu Nanjing 211100,China)

机构地区：[1]江苏方天电力技术有限公司,江苏南京211102

出　　处：《现代防御技术》2021年第3期98-104,共7页Modern Defence Technology

摘　　要：针对小型无人机威胁低空领域且在复杂电磁环境下难识别的问题,提出了一种基于卷积神经网络的信号识别算法。首先,分析了无人机应用背景下直接序列扩频信号的特性,并采用短时傅里叶变换获取信号时频特征;然后,利用提出的能量阈值降噪法降低时频特征中噪声成分;最后,设计了卷积神经网络用于分类识别。仿真结果表明,该算法在信噪比为-6 dB时可达到0.97的识别率,具备较强的鲁棒性和低信噪比环境适应性,其性能显著优于传统算法。Aiming at the problem of small UAVs threatening the low-altitude field and its difficulty in recognition under complex electromagnetic environment,a signal recognition algorithm based on convolutional neural network(CNN)is proposed.The characteristics of the direct sequence spread spectrum(DSSS)signal under the application of UAVs are analyzed,and the signal time-frequency characteristics are obtained by short-time Fourier transform(STFT).The energy threshold noise reduction method proposed is used to reduce the noise in the time-frequency features.The CNN is designed for classification and recognition.Simulation results show that the algorithm has strong robustness and low signal-to-noise ratio environmental adaptability,and its performance is significantly better than traditional algorithms.When the signal-to-noise ratio(SNR)is-6 dB,the recognition rate reaches 0.97.

关 键 词：小型无人机 直接序列扩频 调制样式识别 卷积神经网络 短时傅里叶变换 

分 类 号：TN911.7[电子电信—通信与信息系统] TJ76[电子电信—信息与通信工程]