RayLoc: Wireless Indoor Localization via Fully Differentiable Ray-tracing

TL;DR

RayLoc introduces a fully differentiable ray-tracing based method for indoor wireless localization, enabling precise scene parameter inference and outperforming traditional methods in accuracy and generalization.

Contribution

It reformulates indoor localization as an inverse ray-tracing problem with a differentiable simulator, improving accuracy over conventional CSI-based approaches.

Findings

Outperforms traditional localization baselines

Generalizes well across different environments

Achieves higher localization accuracy

Abstract

Wireless indoor localization has been a pivotal area of research over the last two decades, becoming a cornerstone for numerous sensing applications. However, conventional wireless localization methods rely on channel state information to perform blind modelling and estimation of a limited set of localization parameters. This oversimplification neglects many sensing scene details, resulting in suboptimal localization accuracy. To address this limitation, this paper presents a novel approach to wireless indoor localization by reformulating it as an inverse problem of wireless ray-tracing, inferring scene parameters that generates the measured CSI. At the core of our solution is a fully differentiable ray-tracing simulator that enables backpropagation to comprehensive parameters of the sensing scene, allowing for precise localization. To establish a robust localization context, RayLoc constructs a high-fidelity sensing scene by refining coarse-grained background model. Furthermore, RayLoc overcomes the challenges of sparse gradient and local minima by convolving the signal generation process with a Gaussian kernel. Extensive experiments showcase that RayLoc outperforms traditional localization baselines and is able to generalize to different sensing environments.