In the complex electromagnetic environment, accurately identifying the interference pattern and then adjusting the communication strategy accordingly are the key links to enhance the anti-interference capability of communication system and ensure the communication quality in the military and civil fields. Aiming at the problems of difficult identification of interference signal type in complex electromagnetic environment, single feature extraction of traditional neural network and insufficient adaptability of low dry signal ratio, an interference style identification model based on multimodal heterogeneous wavelet decomposition neural network was proposed. Based on a multimodal feature extraction framework, the proposed model integrated temporal, spectral, and transient features of the signal. A residual mechanism, a three-level cascaded bidirectional long short-term memory (Bi-LSTM), and a multi-scale wavelet decomposition network were incorporated to achieve deep feature mining, while a channel attention mechanism enhanced feature representation. Experiments demonstrate that the model outperforms traditional residual networks and recent heterogeneous models in accuracy, sample efficiency, and robustness under low dry-to-signal ratio conditions
