EAFLoc: A Few-shot Fingerprint Localization Approach with Environmental Awareness

Localization technology is the foundation of location-based services, which plays a crucial role in various fields such as mobile communication and the Internet of Things(IoT). Various approaches have been proposed to achieve precise localization, such as GNSS-based, Time-based, Angle-based, and Proximity-based approaches. However, there are specific drawbacks, including strict time synchronization requirement, susceptibility to signal reflections, and high demands on infrastructure. As a supplement, RSSI-based and Image-based localization approaches have been extensively researched and utilized in recent years. Nevertheless, these approaches are often limited by labor-intensive annotations and researches on utilizing RSSI and image modalities to achieve high-precision localization are also limited. Additionally, there are few researches on localization under few-shot and zero-shot conditions. Therefore, in this paper, we propose EAFLoc, an environment-aware few-shot multimodal fingerprint localization approach. EAFLoc addresses the challenge of efficient modality information fusion. Also, data augmentation algorithms are designed for few-shot scenarios, and we further explore localization under zero-shot scenarios with channel prediction methods. Finally, the superiority and effectiveness of EAFLoc are validated on a real dataset. The experimental results demonstrate that EAFLoc achieves better performance compared to the traditional approaches.