Transactions of Nanjing University of Aeronautics & Astronautics
Mission-Level UAV Intent Recognition Using Image-Trajectory Fusion
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Abstract:
Low-altitude traffic management requires timely inference of both unmanned aerial vehicle （UAV） platform type and likely mission intent. Declared flight plans can provide intent information for cooperative operations， but non-cooperative， abnormal or partially observed UAVs require inference from observable evidence. The existing surveillance methods mainly detect or track UAVs， classify platform type， or predict trajectories. They therefore remain limited when different missions share similar short-term motion and appearance alone lacks temporal context. This study introduces MCFNet， a multimodal framework that combines an external-view RGB image with an 8 s trajectory history for mission-level UAV intent recognition. Its bidirectional cross-attention fusion links local visual regions with trajectory time steps before classification， allowing cues such as payload state or spray effects to be interpreted with the relevant maneuver segments. Modality dropout reduces single-modality shortcuts， and a consistency loss penalizes invalid type-intent pairs. On 7 290 simulated samples covering three UAV types and 15 intents， MCFNet achieves 89.63%Keywords:
Project Supported:
This work was supported by the National Natural Science Foundation of China（No.52102384），the Project for Enhancing R&D Capabilities in Green， Low-Carbon and New Energy Technologies（No.2025GH-JSHZ-02）， and the Xihua University Education and Teaching Reform Project （Special Initiative on Industry-Education Integration）（No.xcjz2025001）.
Clc Number:
V279
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