摘要： 随着软件无线电技术和互联网技术的发展，有大量的互联网接收机接入互联网，并可任意访问其频谱瀑布图数据。频谱瀑布图是信号频域和时域特性的一种展示方式，将不同频率的信号以图像的方式直观地进行展示，为了更好地监测到频谱瀑布图中的特定信号，需要对频谱瀑布图中的特定信号进行智能识别。通过深度学习技术实现了对频谱瀑布图中特定信号识别，在信噪比大于5 dB时，识别准确率大于90%。

中图分类号：TN924；TP391 文献标志码：A DOI: 10.16157/j.issn.0258-7998.234739
中文引用格式： 宁昭义，许国宏，王耀磊. 基于深度学习的互联网接收机中特定信号识别[J]. 电子技术应用，2024，50(5)：66-70.
英文引用格式： Ning Zhaoyi，Xu Guohong，Wang Yaolei. Specific signal recognition in internet receivers based on deep learning[J]. Application of Electronic Technique，2024，50(5)：66-70.

Specific signal recognition in internet receivers based on deep learning

Abstract： With the development of Software Defined Radio（SDR） technology and internet technology, a large number of internet receivers are connected to the internet and can access their spectrum waterfall data at will. Spectral waterfall plot is a way to display the frequency domain and time domain characteristics of signals, visually displaying signals of different frequencies in the form of images. In order to better monitor specific signals in the spectral waterfall plot, it is necessary to intelligently identify specific signals in the spectrogram. This article uses deep learning technology to recognize specific signals in the waterfall spectrum, and the recognition accuracy is more than 90% when the signal-to-noise ratio is more than 5 dB.

Key words : SDR；deep learning；internet receiver；waterfall