GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization
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 scorin...
| Authors | Chenghong Bian, Chaozheng Wen, Hongze Chen et al. |
|---|---|
| Primary category | eess.SP |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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