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 demonstrate 42.96% lower average localization error across diverse indoor locales and heterogeneous devices.

翻译：室内定位是诸多嵌入式应用（如资产追踪、应急响应和实时导航）中的关键任务。本文提出一种新颖的基于指纹的室内定位框架SANGRIA，该框架采用堆叠自编码器神经网络结合梯度提升树。我们的方法旨在克服设备异构性挑战——这一问题会导致用于定位的嵌入式设备间无线信号测量存在不确定性。我们将SANGRIA与多种先进框架进行比较，结果表明，在多样化室内场景和异构设备条件下，其平均定位误差降低了42.96%。