FIM-JFF: Lightweight and Fine-Grained Visual UAV Localization Algorithms in Complex Urban Electromagnetic Environments

Unmanned aerial vehicles (UAVs) are a key driver of the low-altitude economy, where precise localization is critical for autonomous flight and complex task execution. However, conventional global positioning system (GPS) methods suffer from signal instability and degraded accuracy in dense urban areas. This paper proposes a lightweight and fine-grained visual UAV localization algorithm (FIM-JFF) suitable for complex electromagnetic environments. FIM-JFF integrates both shallow and global image features to leverage contextual information from satellite and UAV imagery. Specifically, a local feature extraction module (LFE) is designed to capture rotation, scale, and illumination-invariant features. Additionally, an environment-adaptive lightweight network (EnvNet-Lite) is developed to extract global semantic features while adapting to lighting, texture, and contrast variations. Finally, UAV geolocation is determined by matching feature points and their spatial distributions across multi-source images. To validate the proposed method, a real-world dataset UAVs-1100 was constructed in complex urban electromagnetic environments. The experimental results demonstrate that FIM-JFF achieves an average localization error of 4.03 m with a processing time of 2.89 s, outperforming state-of-the-art methods by improving localization accuracy by 14.9% while reducing processing time by 0.76 s.