Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices
This addresses the challenge of maintaining accurate indoor positioning for users and assets over extended periods, representing an incremental improvement in robustness.
The paper tackles the problem of long-term degradation in WiFi-based indoor localization accuracy due to signal variability and access point changes, achieving up to a 40% reduction in accuracy degradation over time compared to state-of-the-art methods without retraining.
Fingerprinting-based indoor localization is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. The superior pairing of ubiquitously available WiFi signals with computationally capable smartphones is set to revolutionize the area of indoor localization. However, the observed signal characteristics from independently maintained WiFi access points vary greatly over time. Moreover, some of the WiFi access points visible at the initial deployment phase may be replaced or removed over time. These factors are often ignored in indoor localization frameworks and cause gradual and catastrophic degradation of localization accuracy post-deployment (over weeks and months). To overcome these challenges, 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 retraining.