AMTL-Loc: Efficient WiFi Indoor Localization with Reduced Fingerprint Collection

Zaipeng Xie, Wenhao Fang, Bingzhe Yu, Yang Ding, Yanling Pan, Wen-Zhan Song

Abstract: Collecting Wi-Fi fingerprints is essential for Wi-Fi-based indoor localization techniques. However, this process can be time-consuming and labor-intensive due to the spatial and tempo-ral variations of Wi-Fi signals caused by environmental factors, interference, and fading. Moreover, the variability of signals emit-ted by different access points can hinder localization accuracy, especially in complex indoor environments. To overcome these challenges, we propose the Attention Mechanism-based Transfer Learning Indoor Localization (AMTL-Loc) framework, which transfers a pre-trained model from a source space to a target space and adapts it using minimal data by extracting redundant information from Wi-Fi fingerprints. Our experimental evalu-ations show that the AMTL-Loc framework can significantly reduce the fingerprint collection workload in diverse indoor environments while maintaining high localization accuracy compared to existing state-of-the-art indoor localization methods. Therefore, our framework offers a promising solution to enhance the efficiency and accuracy of Wi-Fi-based indoor localization techniques.