Denoising Method for Detecting Low-Probability-of-Intercept Radar Signal Using Convolutional Neural Network

Estimation of radar signal characteristics holds significant importance in the field of electronic warfare support. However, the emergence of low-probability-of-intercept (LPI) radar has witnessed widespread adoption, aiming to minimize the possibility of detection. LPI radar operates with a low signal-to-noise ratio and employs various modulation schemes, making it challenging to estimate signal characteristics. To address this problem and enhance the detection probability, this paper presents a convolutional neural network approach for noise reduction in LPI radar signals with various modulation schemes. We conduct simulations to validate the effectiveness of the proposed method.