DETECTION OF VLF TRANSMITTERS USING THE ViTRANSFORMER DEEP LEARNING ALGORITHM

Radio signals of very low frequency (VLF) are used for monitoring the lower ionosphere. Their analysis provides information about ionospheric perturbations in the areas between the transmitter and the receiver of the observed signal, caused by phenomena in space and within Earth’s layers. The research presented in this paper demonstrates the capabilities of the ViTransformer neural network for detecting VLF transmitters using images. The images used in this study represent a two-dimensional visualization of the signal emitted by a VLF transmitter and received by a corresponding receiver. For training and testing, the data recorded at the Institute of Physics in Belgrade were used. The database employed contains recorded signals emitted from three different VLF transmitters located in different parts of the world (Germany, the United States, and Australia). The research results show that using the ViTransformer neural network makes it possible to perform classification with high accuracy, reaching nearly 95%.