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

arXiv:2403.01348v1 Announce Type: new
Abstract: Indoor localization is a critical task in many embedded applications, such as asset tracking, emergency response, and realtime navigation. In this article, we propose a novel fingerprintingbased 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 …

abstract applications article arxiv autoencoder boosting cs.ai cs.lg eess.sp embedded emergency emergency response framework gradient gradient boosted trees localization navigation networks neural networks novel realtime tracking trees type