다중 안테나 다이버시티 통합을 이용한 딥러닝 기반 저피탐 레이다 신호 변조 분류 연구

This paper proposes a deep learning-based modulation classification system designed to effectively detect and classify Low Probability of Intercept (LPI) radar signals in Electronic Warfare (EW) environments. While previous studies primarily focused on signals captured from a single antenna, practical battlefield environments typically involve multiple antennas spatially distributed to simultaneously intercept incoming signals. Therefore, this study emphasizes the utilization of diversity gain achieved through integrating signals from multiple antennas. To this end, we propose a method that employs various backbone networks, such as CNN, EfficientNet-B2, and ResNet-50, to extract feature vectors from each antenna signal, followed by feature integration using methods including LSTM, concatenation, and average pooling. Experimental results integration using methods including LSTM, concatenation, and average pooling. Experimental results demonstrate that, under challenging low-SNR conditions (specifically, at an SNR of-10 dB), the classification accuracy significantly improves from approximately 65% with a single CNN-based channel to 87.6% with the proposed CNN-LSTM-based five-channel diversity system. Thus, our research empirically verifies that the proposed multi-antenna integration method notably outperforms the conventional single-antenna approach.