Wi-Fi Fingerprint Update for Indoor Localization via Domain Adaptation

Abstract

Wi-Fi signals vary over time due to multipath fading and dynamic indoor environment. Hence in the long-run deployment of Wi-Fi fingerprinting localization, to retain high accuracy the fingerprint database has to be updated regularly, which is usually labor-intensive and time-consuming. In this paper, we propose a novel unsupervised domain adaptation model TransLoc for Wi-Fi fingerprint update, to keep high accuracy yet at a low cost. TransLoc consists of a feature extractor, a generator, a discriminator, and a location predictor. The feature extractor learns domain-invariant features by cooperating with other components. To further guarantee localization accuracy, the location predictor is designed as a semi-supervised regressor with three parallel sub-modules. We carry out extensive experiments in two typical real-world indoor environments with a total area of over 8,200 $m^{2}$ across three months. Experimental results show that with only an initial fingerprint database and current unlabeled fingerprints, TransLoc maintains high localization accuracy at a low cost in the long run.