Few-Shot Recognition and Classification Framework for Jamming Signal: A CGAN-Based Fusion CNN Approach

Subject to intricate environmental variables in global navigation satellite systems (GNSS), the precise recognition of jamming signals holds paramount significance in the effective implementation of anti-jamming strategies. In light of this imperative, we propose an innovative fusion method based on conditional generative adversarial network (CGAN) and convolutional neural network (CNN), which aims to deal with the difficulty in applying deep learning (DL) algorithms due to the instantaneous nature of jamming signals in practical communication systems. Compared with previous methods, our method embeds jamming category labels to constrain the range of generated signals in the frequency domain by using the CGAN model, which simultaneously captures label-conditioned information while learning the distribution of signal data thus achieving an 8% improvement in accuracy over a semi-supervised GAN baseline even when working with a few-shot dataset. Real-world satellite communication scenarios are simulated by adopting a hardware platform, and we validate the proposed method by using the resulting time-domain waveform data. The experimental results indicate that our method performs exceptionally well, demonstrating significant potential for practical application in GNSS.