AutoLoc: Enabling Low-Effort Device and User Localization with Commercial Wi-Fi

Wi-Fi localization and tracking are increasingly becoming the key enabler for intelligent services and lead to a wide range of pioneering techniques. However, most existing systems rely on prior knowledge of the device position (DP) and the user’s initial position (IP) to model, which is often unavailable in dynamic real-world deployments. To overcome this limitation, we propose AutoLoc, a system that jointly estimates DP and IP without intensive manual calibration or dedicated hardware. Our core insight lies in the spatial heterogeneity of Wi-Fi signal features. Specifically, we develop a reconstruction pipeline, which first predicts user trajectories based on observed features, then infers positions by aligning reconstructed features from trajectories with observations. To further enhance accuracy and efficiency, we introduce an alternating iteration and multi-trace optimization. We build a prototype with the COTS device and conduct experiments across diverse real-world scenarios. Extensive experiments demonstrate that our system achieves localization errors of 0.43m for the device and 0.45m for the initial position with only one labeled link, while also achieving tracking accuracy comparable to state-of-the-art systems. AutoLoc aims to offer a low-cost, practical solution for Wi-Fi sensing and paves the way toward plug-and-play deployment.