Self-supervised masked-signal pre-training, including on out-of-domain communications data, improves few-shot radar signal classification accuracy by up to 17.5% relative to no pre-training.
We also demonstrated the viability of RF domain adaptation for enhancing signal classification perfor- mance when no target domain data was used for pre-training
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Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation
Self-supervised masked-signal pre-training, including on out-of-domain communications data, improves few-shot radar signal classification accuracy by up to 17.5% relative to no pre-training.