Automatic Update for Wi-Fi Fingerprinting Indoor Localization via Multi-Target Domain Adaptation

Abstract

Wi-Fi fingerprinting system in the long term suffers from gradually deteriorative localization accuracy, leading to poor user experiences. To keep high accuracy yet at a low cost, we first study long-term variation of access points (APs) and characteristics of their Wi-Fi signals through over-one-year experiments. Motivated by the experimental findings, we then design MTLoc, a Multi-Target domain adaptation network-based Wi-Fi fingerprinting Localization system. As the core, MTDAN (Multi-Target Domain Adaptation Network) model adopts the framework of generative adversarial network to learn time-invariant, time-specific, and location-aware features from the source and target domains. To enhance the alignment among the source and targets, two-level cycle consistency constraints are proposed. Hence, MTDAN is able to transfer location knowledge from the source domain to multiple targets. In addition, domain selection and outlier detection are designed to avoid explosive growth of storage for targets and to limit the impact of random variations of Wi-Fi signals. Extensive experiments are carried out on five datasets collected over two years in various real-world indoor environments with a total area of 8, 350 m2. Experimental results demonstrate that MTLoc retains high localization accuracy with limited storage and training cost in the long term, which significantly outperforms its counterparts. We share our dataset to the community for other researchers to validate our results and conduct further research.