Automatic Extraction of VLF Constant‐Frequency Electromagnetic Wave Frequency Based on an Improved Vgg16‐Unet.

Constant Frequency Electromagnetic Waves (CFEWs) refer to electromagnetic waves with a constant frequency. Man‐made CFEWs are mainly used in wireless communication, scientific research, global navigation and positioning systems, and military radar. CFEWs exhibit horizontal line characteristics higher than the background on spectrograms. In this study, we focus on Very Low Frequency (VLF) waveform data and power spectral data collected by the China Seismo‐Electromagnetic Satellite (CSES) Electromagnetic Field Detector (EFD). We utilize deep learning techniques to construct an improved Vgg16‐Unet model for automatically detecting horizontal lines on time‐frequency spectrogram and extracting their frequencies. First, we transform waveform data into time‐frequency spectrogram with a duration of 2 s using Short‐Time Fourier Transform. Then, we manually label horizontal lines on the time‐frequency spectrogram using the Labelme tool to establish the dataset. Next, we establish and improve the Vgg16‐Unet deep learning model. Finally, we train and test the model using the dataset. Statistical experimental results show that the error rate of line detection is 0, indicating high reliability of the model, with fewer parameters and fast computation speed suitable for practical applications. Not only do we detect lines through the model, but we also obtain their frequencies. Additionally, in batch‐generated power spectrogram of CFEWs, we discover some unstable phenomena such as frequency shifts and fluctuations, which contribute to understanding the propagation mechanism of CFEWs in the ionosphere and improving the accuracy of related systems. Plain Language Summary: Since its launch in 2018, China's first seismic electromagnetic satellite (CSES) has received a large amount of ionospheric disturbance during its over 5 years in orbit, including a significant number of CFEWs. Based on the characteristic high‐level spectral line features of CFES in the spectrogram, this study primarily employs deep learning methods to generate spectrogram from the data collected by CSES, detect CFEWs, and extract their frequencies. Furthermore, based on the extracted frequencies, it was discovered during the bulk generation of their power spectrogram that some of these CFEWs exhibit very stable signals, while others show frequency fluctuations and frequency drift phenomena. These instabilities can significantly affect system performance, leading to phenomena such as signal distortion or loss in communication systems, difficulties in target detection and tracking in radar systems, and inaccurate positioning in navigation systems. Therefore, this study contributes to understanding the characteristics of CFEWs in electromagnetic propagation and helps improve the accuracy of related systems. Key Points: The constant‐frequency electromagnetic wave appear as horizontal straight lines on the spectorgramA deep learning algorithm is used to detect horizontal lines on the time‐frequency spectorgramExtract the frequency of the constant‐frequency electromagnetic waves generating these straight lines [ABSTRACT FROM AUTHOR]