SANGRIA: Stacked Autoencoder Neural Networks With Gradient Boosting for Indoor Localization

Indoor localization is a critical task in many embedded applications, such as asset tracking, emergency response, and real-time navigation. In this letter, we propose a novel fingerprinting-based framework for indoor localization called SANGRIA that uses stacked autoencoder neural networks with gradient-boosted trees. Our approach is designed to overcome the device heterogeneity challenge that can create uncertainty in wireless signal measurements across embedded devices used for localization. We compare SANGRIA to several state-of-the-art frameworks and demonstrate 42.96% lower average localization error across diverse indoor locales and heterogeneous devices.