Compound Interference Recognition Method for AAV Communication Based on Multi-Modal Multi-Label Learning Under Low INR

Unmanned aerial vehicle (UAV) communications are susceptible to malicious compound interference signals due to the complexity and variability of the electromagnetic environment and the openness of the air-to-ground wireless channels, leading to degradation of communication quality. Therefore, effective detection and accurate recognition of compound interference are the key to ensuring secure UAV communication in complex environments. However, existing deep learning-based interference recognition algorithms suffer from fewer recognizable compound interference types, a large number of model parameters, and lower interference recognition accuracy under low interference-to-noise power ratio (INR) conditions. This paper proposes a malicious compound interference recognition method for UAV communication based on multi-modal multi-label learning and designs a lightweight multi-modal interference recognition network. By introducing a multi-label learning mechanism and making full use of the complementary information between different modalities of the signal, the method can achieve more flexible, accurate and stable recognition of compound interference signals under low INR. We construct both simulation and real measured datasets containing 31 classes of compound interference signals, and conduct simulation experiments with sufficient samples, insufficient samples, and different training strategies. The results demonstrate that the proposed method enhances the recognition accuracy of UAV communication compound interference under low INR and across different training datasets, all while maintaining a small number of model parameters.