Electromagnetic signal recognition using multimodal tri-branch semantic fusion network in the UAV-assist integrated sensing and communication systems

Driven by the proliferation of integrated sensing and communication (ISAC) systems, the accurate recognition of unauthorized unmanned aerial vehicle (UAV) signals in dynamic electromagnetic environments has emerged as a critical challenge for spectrum security and cognitive radio applications. Conventional automatic modulation recognition (AMR) frameworks suffer from significant performance degradation in low signal-to-noise ratio (SNR) regimes and exhibit limited adaptability to resource-constrained edge computing platforms. To address these limitations, we propose a novel Multimodal Tri-branch Fusion Network (MTF-Net) architecture that synergistically integrates time-frequency analysis with statistical feature learning. The framework systematically processes binarized time-frequency images (B-TFIs) and higher-order cumulant vectors through three collaboratively operating branches: (1) A primary temporal feature extractor employing dilated convolution-residual blocks (DCRBlocks) with hierarchical dilatation factors, incorporating channel attention mechanisms to dynamically emphasize discriminative temporal patterns; (2) Dual auxiliary branches based on Edge-Transformer modules (ETFormers), which achieve efficient spatial-structural learning through depthwise separable convolutions (DSC) while capturing long-range spectral dependencies via additive attention mechanisms with linear complexity; (3) A hierarchical fusion module implementing cross-branch feature recalibration through learnable parameter matrices. Extensive Monte Carlo experiments demonstrate that our MTF-Net significantly outperforms traditional methods in recognition accuracy for radar and communication signals under low SNR conditions, establishing a new benchmark for lightweight AMR solutions in ISAC systems.