Multifunction Radar Working Mode Recognition with Unsupervised Hierarchical Modeling and Functional Semantics Embedding Based LSTM

Recognition of multifunction radar (MFR) working modes provides essential information to assess the behavior of a non-cooperative MFR. However, it typically depends on substantial previously intercepted waveforms. To enable better recognition with limited pre-observations, this paper proposes a radar-semantics-modeled LSTM recognition algorithm with embedding radar waveform functionality features and utilizing contextual recognition of an LSTM network. On the one hand, unsupervised hierarchical modeling and functional semantics embedding extract MFR behavior units and embed the artificial functional semantics of waveforms based on experts’ knowledge about waveform functionalities. On the other hand, an LSTM network captures static and dynamic features within sequences of radar semantic data, recognizing the MFR working mode. Experimental comparisons on preset datasets illustrate that the proposed algorithm adapts more effectively to parameter variations and migrates the recognition between the MFRs with similar functional behavior features.