Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices

WiFi fingerprinting-based indoor localization on smartphones is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. Unfortunately, the transmitted signal characteristics from independently maintained WiFi access points (APs) vary greatly over time. Moreover, some of the WiFi APs visible at the initial deployment phase may be replaced or removed over time. These factors are often ignored and cause gradual and catastrophic degradation of indoor localization accuracy post-deployment, over weeks and months. We propose a Siamese neural encoder-based framework that offers up to 40% reduction in degradation of localization accuracy over time compared to the state-of-the-art in the area, without requiring any re-training.