ABSTRACT Automatic noncooperative analysis of intercepted radar signals is essential for intelligent equipment in both military and civilian domains. Accurate modulation identification and parameter estimation enable effective signal classification, threat assessment and the development of countermeasures. In this paper, we propose a symbolic approach for radar signal recognition and parameter estimation based on a vision‐language model that combines context‐free grammar with time‐frequency representation of radar waveforms. The proposed model, called Sig2text, leverages the power of vision transformers for time‐frequency feature extraction and transformer‐based decoders for symbolic parsing of radar waveforms. By treating radar signal recognition as a parsing problem, Sig2text can recognise the types and estimate the parameters of signals with arbitrarily complex modulations. We evaluate the performance of Sig2text on a synthetic radar signal dataset and demonstrate its effectiveness in various scenarios.
Yuan et al. (Thu,) studied this question.
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