CLMFN: An Intelligent Discrimination Method of Deception Jamming Based on Multi-modal Systems

The existing anti-jamming methods are based on single modality, lacking cross-modalities feature extraction and general discriminant algorithms. In order to increase the diversity of the discriminant scenarios, a multi-modal fusion model of jamming countermeasure is proposed. The model can collaboratively process cross-modalities information and prevent the loss of discrimination performance when lacking partial modalities, thus ensuring the all-round utilization of information. Specifically, a convolutional neural network and an attention-based bidirectional long short-term memory network model are constructed to extract complex envelope features of signal modality and kinematic time-series features of data modality, respectively. At the same time, the two features are fused to learn joint representations between cross modalities. The constraint condition is added to the loss function which can ensure various feature vectors are highly correlated, so that the discrimination remains good even facing the single modality. Extensive experimental results show that the performance of proposed multi-modal network is superior to the single modal network. Specifically, the proposed model exhibits robustness for different modalities and can intelligently adjust the dependence on different modalities.