Low Probability of Interception Radar Signals Detection, Comparison of a YOLOv8 Model and a Conventional Signal Processing Method

Radar interception plays a critical role in electronic warfare by capturing and analyzing enemy radar emissions. Low Probability of Intercept (LPI) signals pose a significant challenge to conventional detection methods due to their design to minimize detectability. This paper explores the benefit of the YOLOv8 algorithm, a state-of-the-art deep learning model, for detecting LPI radar signals in time-frequency images. A comparative analysis to assess detection performance is conducted with a common sense solution based on energy detector which is the Generalized Likelihood Ratio Test (GLRT) when the signal is unknown. Results demonstrate that YOLOv8 achieves detection with a notable improvement in Peak Signal-to-Noise Ratio (PSNR) for every waveform considered.