RayLoc: Wireless Indoor Localization via Fully Differentiable Ray-tracing

The rise of digital twins, particularly in smart manufacturing and automated logistics, demands ultra-high-precision indoor localization that existing technologies struggle to provide. Conventional wireless localization methods, which rely on channel state information to estimate a limited set of parameters, neglect critical details of the sensing scene, leading to suboptimal accuracy. This paper presents RayLoc, a novel approach that addresses this limitation by reformulating wireless indoor localization as an inverse problem. By leveraging the digital twin of an environment, our method infers the precise scene parameters that generate the measured CSI, enabling highly accurate localization. At the core of our solution is a fully differentiable ray-tracing simulator that enables backpropagation to all sensing-critical parameters, including target locations. To establish a robust localization context, RayLoc first constructs a high-fidelity sensing scene by refining a coarse-grained background model, often derived from the initial digital twin. Furthermore, to overcome optimization unfriendliness of CSI-based loss landscapes, RayLoc introduces Gaussian kernal smoothing combined with an adaptive convergence strategy to mitigate sparse gradients and local minima. Extensive experiments showcase that RayLoc not only outperforms traditional localization baselines but is also able to generalize to different sensing environments, paving the way for its application in next-generation, high-accuracy-dependent systems.