Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data

The cost, flexibility, and efficiency of modern UAVs make them attractive across many applications, but their proliferation has driven a rising number of malicious or accidental incidents, making UAV detection and classification mechanisms essential. Individual sensing modalities each present complementary limitations, and existing detection systems typically rely on a single sensor or fuse modalities only at the decision level, leaving the feature-level fusion of heterogeneous image and radar detectors largely unexplored. We propose a deep neural network that fuses high-level features extracted from the individual object-detection and classification models of thermal, optronic, and radar sensors. A CNN-based architecture combines the three modalities by stacking the thermal and optronic image features along the channel axis prior to fusion with the radar features. Evaluated on a real-world multi-sensor dataset, the proposed three-modality fusion model attains an F1-score of 0.95, compared to 0.93 for the dual-modality (thermal-optronic) configuration and 0.91 for the best-performing single-sensor (thermal) baseline, confirming that fusing complementary sensor features yields measurable gains in UAV classification performance.