A Cross-Modality Contrastive Learning Method for Radar Jamming Recognition

The discernment of jamming signal was important for the downstream tasks. Though great improvement was achieved by deep learning previously, these methods need large amounts of signals with label information. It was yet difficult to be met in the practical applications. To solve the problem, a cross-modality contrastive learning method is proposed in this article. The signalwise hierarchy and the imagewise hierarchy were presented to learn the features from IQ tensor and TF image, respectively. The cross-domain features were then aggregated subsequently. The obtained features were delivered to the learning architecture. It was composed of the pretraining and the fine-tuning. The signals without label information were first used to pretrain a base model. The similarity loss that made the positive more similar to the signal than the negative was presented to optimize the model. The pretrained model was then fine-tuned by a small amounts of labeled signals. The recognition task can be then achieved accordingly. Therefore, the labeled signals and the unlabeled ones were jointly exploited. Likewise, two different kinds of modal data were unified into a single framework. Multiple rounds of experiments were finally performed. The results thrown light on the superiority of the proposed method over the standard and recent techniques.