基于频域叠加和深度学习的频谱信号识别  

Spectrum signal identification based on frequency-domain superposition and deep learning

作　　者：周宇航 侯进[1,2] 李嘉新 李惠森 Zhou Yuhang;Hou Jin;Li Jiaxin;Li Huisen(IPSOM Laboratory of School of Information Science&Technology,Southwest Jiaotong University,Chengdu 611756,China;National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学信息科学与技术学院智能感知智慧运维实验室,成都611756 [2]西南交通大学综合交通大数据应用技术国家工程实验室,成都611756

出　　处：《计算机应用研究》2023年第3期874-879,共6页Application Research of Computers

基　　金：国家重点研发计划资助项目(2020YFB1711902);四川省科技计划资助项目(2020SYSY0016)。

摘　　要：针对频谱监测领域频带较宽、接收机采样步进受限而造成的宽带频谱信号难以识别的问题,提出了一种频域叠加预处理和目标检测相结合的频谱信号识别方法。该方法利用频域叠加对频谱数据进行多帧叠加来突出频谱中的弱信号,将叠加处理后的频谱图像送入改进的目标检测网络中进行信号类型的识别。经过实验证明,此方法能够有效地识别7种类型的频谱信号,提出的频域叠加预处理能够提升目标检测算法的精度,并提高频谱中弱信号的识别能力,在信噪比为6 dB时,算法能够达到89.7%的平均识别率。In the field of spectrum monitoring, it is difficult to identify wideband spectrum signals because of wide frequency band and limited receiver sampling steps.This paper proposed a spectrum signal identification method combining frequency-domain superposition preprocessing and object detection.The domain superposition method accumulated multiple frames of spectrum data to highlight weak signals in the spectrum, then sent the spectrum images to the improved object detection network for signal type identification.Experiments show that the proposed method can effectively identify 7 kinds of spectrum signals.The proposed frequency-domain superposition preprocessing can improve the accuracy of the object detection algorithm and improve the identification ability of weak signals in the spectrum.When the signal-to-noise ratio is 6 dB,the algorithm can achieve an average recognition rate of 89.7%.

关 键 词：宽带频谱检测 频域叠加 卷积神经网络 目标检测 

分 类 号：TP393.04[自动化与计算机技术—计算机应用技术]