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영어

This paper proposes a vision-language model for the joint recognition of Low Probability of Intercept (LPI) radar signals through time-frequency distribution (TFD)-text alignment. The proposed framework unifies waveform classification and signal parameter estimation by aligning TFD spectrograms with hierarchical textual prompts in a shared embedding space. To support both general waveform type recognition and fine-grained parameter inference, we introduce a prompt dropout strategy that balances rich and simple prompts during training. Evaluated on multiple TFD representations including SPWVD, CWD, and SAFI, the model demonstrates high accuracy and interpretability across both tasks. This unified approach offers a compact, extensible solution for LPI radar signal understanding.