Radar Jamming Recognition via Cross-Modality Contrast Learning

Abstract

The discernment of radar jamming signal played an important role for the downstream tasks. Great performance was achieved by the deep learning methods. Yet large amounts of labeled signals were needed. To solve the problem, a new cross-modality contrast learning method was proposed in this paper. The signal-wise hierarchy, and the image-wise hierarchy were developed to extract the features from IQ data (In-phase & quadrature) and TF-image (Time-frequency). The cross-domain features were then combined. The fused features were delivered to the learning phase. It was composed of the pre-training and the fine-tuning. The unlabeled signals were first employed to optimize the similarity loss to make the positive sample mores similar to the signal than the negative samples. The pre-trained model was then fine-tuned to the recognition task. The proposed method was demonstrated to provide impressive performance by multiple comparative studies.