基于多模态信息融合的无人机探测技术综述OA

With the rapid development of the low-altitude economy,the number of low,slow,and small(LSS)civilian multi-rotor unmanned aerial vehicles(UAVs)has increased dramatically,and the security threats posed by"black fly-ing"(illegal flights)have become increasingly prominent.Single-modal detection technologies are constrained by physi-cal boundaries and can no longer meet the requirements of complex tasks,making multimodal information fusion detec-tion the mainstream direction in the industry.This paper systematically reviews the current development status,techno-logical evolution,and future challenges of multimodal detection technologies for LSS civilian multi-rotor UAVs—the core targets of civilian low-altitude"black flying".This paper first deeply analyzes the performance boundaries and complementary characteristics of single modalities—such as radar,electro-optical,radio frequency(RF),and acoustics—in long-range detection,high-precision recognition,passive detection,and cost control.Second,it highlights the evolu-tionary logic of multimodal fusion technologies,from the decision(weighted fusion)and feature(heterogeneous fea-ture extraction and interaction)levels to the hybrid level(multi-stage coupling),comparing the advantages,disadvan-tages,and applicable scenarios of each level.Finally,it details mainstream public datasets such as Anti-Drone and MMAUD,analyzing core elements including sensor configurations,task types,and data synchronization.The study finds that feature-level fusion is currently the mainstream paradigm for improving detection accuracy;however,it still faces bottlenecks in terms of computational resource consumption and heterogeneous data alignment.Hybrid-level fu-sion possesses a complex architecture;nevertheless,it is the key breakthrough to balancing accuracy and efficiency.Through the analysis of typical cases such as"RF+Optical"field tests,the feasibility of feature joint enhancement and cross-modal track fusion schemes in complex environments is verified,with a detection probability exceeding 95%.The conclusion points out that multimodal fusion effectively solves the problems of single-modal detection,such as suscepti-bility to environmental interference and high missed-detection rates,significantly enhancing the robustness of the sys-tem.Future research should deepen the hybrid-level fusion architecture,promote the application of large-model tech-nologies in multimodal data association and feature matching,and focus on spatial heterogeneous fusion strategies,thereby creating a reference for building an intelligent,all-domain perceived low-altitude regulatory system.