GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization
2026-08-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors introduce GLocFM, a new method that improves locating WiFi signals indoors by using both the wireless measurements and the 3D shape of the environment. They treat locating the signal source as finding the spot that best matches what is observed, using a learned function that compares expected and actual signal patterns. Their model also handles issues like time measurement errors and is trained on many simulated indoor scenes. Tests on synthetic and real data show that GLocFM cuts localization errors almost in half compared to strong existing methods. The authors also show their method works well with varying numbers of receivers and hardware setups.
WiFi localizationnon-line-of-sight (NLoS)3D point cloudmaximum-likelihood estimationangle-of-arrival (AoA)time-of-flight (ToF)scene geometrywireless propagationhierarchical encodersynthetic dataset
Authors
Chenghong Bian, Chaozheng Wen, Hongze Chen, Jun Zhang
Abstract
Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes. To bridge this gap, we propose GLocFM, a Geometry-aware Localization Foundation Model, which jointly exploits WiFi measurements and scene geometry represented as a 3D point cloud. We formulate localization as a maximum-likelihood (ML) estimation problem, where the goal is to find a transmitter position that maximizes the likelihood of the wireless observations conditioned on the scene geometry. The likelihood of a candidate transmitter position is calculated by a learned scoring function that matches the observed delay--angle-of-arrival (AoA) spectrum against the spectrum predicted for that candidate. A hierarchical scene encoder extracts propagation-relevant features to produce geometric priors for LoS and one-bounce reflection paths. For scenarios with imperfect synchronization, we further introduce a time-of-flight (ToF)-robust GLocFM model to handle unknown ToF offsets. GLocFM is trained on a multi-modal synthetic indoor localization dataset comprising 221 diverse scenes whose associated wireless signals are generated using Sionna RT. On both synthetic and the NeRF$^{2}$ dataset based on real measurements, GLocFM reduces mean 3D localization error relative to one of the state-of-the-art localization baselines by 49.5\% and 48.8\%, respectively. Ablations across different number of receiver, bandwidths, and array sizes further demonstrate the effectiveness and robustness of the proposed framework.