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The precise classification of jamming signals holds paramount significance inthe effective implementation of anti-jamming strategies within communicationsystems subject to intricate environmental variables. In light of thisimperative, we propose an innovative fusion algorithm based on conditionalgenerative adversarial network (CGAN) and convolutional neural network (CNN) tosolve the problem of difficulty in applying deep learning (DL) algorithms dueto the instantaneous nature of jamming signals in practical communicationsystems. Compared with previous methods, our algorithm achieved an 8%improvement in accuracy even when working with a limited dataset. Unlikeprevious research, we have simulated real-world satellite communicationscenarios using a hardware platform and validated our algorithm using theresulting time-domain waveform data. The experimental results indicate that ouralgorithm still performs extremely well, which demonstrates significantpotential for practical application in real-world communication scenarios.

Few-Shot Recognition and Classification of Jamming Signal via CGAN-Based Fusion CNN Algorithm