Annotation-Free Indoor Radio Mapping via Physics-Informed Trajectory Inference

TL;DR

This paper introduces a physics-informed, label-free indoor radio mapping method that infers user trajectories solely from CSI data, eliminating the need for extensive site surveys or IMU sensors.

Contribution

It proposes a novel trajectory inference framework leveraging local spatial continuity of multipath propagation, without user location labels or IMU data.

Findings

Achieves an average localization error of 0.88 meters.

Reduces propagation-parameter estimation error to 6.68%.

Outperforms existing channel-embedding and IMU-assisted methods.

Abstract

Constructing indoor radio maps traditionally requires extensive site surveys with precise user-location labels, making the calibration process costly and time-consuming. Existing calibration-reduction methods either depend on partial location annotations or exploit inertial measurement units (IMUs) to provide relative motion cues; however, IMU-assisted solutions are constrained by hardware availability, device-level access restrictions, and accumulated sensor drift. In this paper, we study a location-label-free indoor radio mapping problem under known access-point deployment geometry and a known walkable spatial domain. We propose a physics-informed trajectory inference framework that uses only Channel State Information (CSI), without relying on user-location labels or IMU measurements. The key idea is to recover the latent spatial coordinates of CSI measurements by exploiting the local spatial continuity of multipath propagation. To this end, we construct a Power-Angle-Delay Profile (PADP) feature distance from MIMO-OFDM CSI and show that, within a local neighborhood and under quasi-static multipath conditions, this distance provides a physically meaningful proxy for small spatial displacements. We then incorporate the PADP-based continuity constraint into a spatially regularized Bayesian inference model for joint trajectory recovery and propagation-parameter estimation. Experiments on a real-world industrial CSI dataset demonstrate that the proposed framework achieves an average localization error of 0.88 m and a relative beam map construction error of 6.68%, improving upon representative channel-embedding and IMU-assisted baselines.