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

2026-05-10

AI Summary

This work addresses the high cost and hardware limitations of conventional indoor radio frequency (RF) map construction, which typically relies on densely sampled measurements with precise positional labels or inertial measurement units (IMUs) prone to drift. The authors propose a novel method that eliminates the need for both positional labels and IMU data by leveraging only MIMO-OFDM channel state information (CSI), given prior knowledge of access point locations and walkable areas. Central to their approach is the introduction of Power-Angle-Delay Profiles (PADPs) as physically grounded proxies for local spatial displacement, integrated within a physics-informed trajectory inference framework. By modeling multipath continuity and employing spatially regularized Bayesian inference, the method jointly optimizes user trajectory and propagation parameters. Evaluated on real-world industrial CSI datasets, it achieves an average localization error of 0.88 meters and a beam pattern reconstruction error of 6.68%, substantially outperforming existing channel embedding and IMU-assisted baselines.

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.

Problem

• indoor radio mapping
• annotation-free
• Channel State Information
• trajectory inference
• location calibration

Innovation

• annotation-free radio mapping
• physics-informed trajectory inference
• Channel State Information (CSI)
• Power-Angle-Delay Profile (PADP)
• spatial continuity