Complex signals discovery,detection and classification based on YOLOv5
With the development of wireless communication technology,the electromagnetic environment has become increasingly complex and various types of signals and noise coexist in the spectrum,posing greater challenges to the signal detection technology of receivers.The detection of communication signals is a crucial part of wireless adversarial processes.Only by correctly detecting signals can subsequent steps such as signal recogni-tion,parameter estimation and demodulation be completed.The main purpose of signal discovery and detection is to determine the presence of signals through broadband spectrum data.In addition,the actual battlefield environ-ment may be filled with signals such as frequency hopping,burst and fixed frequency,and different signals corre-spond to different processing methods.In traditional algorithms,energy detection is commonly used,but it is dif-ficult to distinguish between noise and signal when the signal-to-noise ratio is low.But in reality,the signal can be detected in the time-frequency graph and the category can be observed.Therefore,a method based on deep learn-ing is proposed that achieves higher accuracy in signal discovery,detection and classification by extracting fea-tures from time-frequency waterfall plots.
signal detectiondeep learningsignal classificationYOLOv5
NETL
NSTL
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