南京航空航天大学学报（英文版）2026，Vol.43Issue(4)：529-545,17.DOI:10.16356/j.1005-1120.2026.04.004
基于图像-轨迹融合的任务级无人机意图识别
Mission-Level UAV Intent Recognition Using Image-Trajectory Fusion
摘要
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%±0.99%intent accuracy,89.41%±1.00%type-intent joint accuracy and 91.33%±1.05%macro-F1,outperforming the strongest late-fusion baseline by 3.04%and achieving a slightly higher mean intent accuracy than the multimodal bottleneck Transformer(MBT)token-level fusion baseline.The results provide an observation-based route to earlier warning and differentiated decision support for low-altitude traffic management.关键词
无人机意图识别/图像-轨迹融合/低空空域/轨迹消歧/领域感知约束Key words
UAV intent recognition/image-trajectory fusion/low-altitude airspace/trajectory disambiguation/domain-aware constraints分类
航空航天引用本文复制引用
唐立,王梓洁,涂彭越..基于图像-轨迹融合的任务级无人机意图识别[J].南京航空航天大学学报（英文版）,2026,43(4):529-545,17.基金项目
This work was supported by the Na-tional 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 Re-form Project(Special Initiative on Industry-Education Inte-gration)(No.xcjz2025001). （No.52102384）
