发表机构
Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有学习式无线定位器无法利用环境几何信息的问题，提出GLocFM模型，联合利用WiFi测量与三维点云场景几何，在两类数据集上较基线降低近50%定位误差，鲁棒性与有效性经消融实验验证。
AI 中文摘要
基于学习的无线定位器常无法利用传播环境的几何信息，限制了其对非视距（NLoS）传播的利用及跨场景泛化能力。为弥补这一差距，我们提出GLocFM（Geometry-aware Localization Foundation Model，几何感知定位基础模型），该模型联合利用WiFi测量值和表示为三维点云的场景几何信息。我们将定位问题建模为最大似然（ML）估计问题，目标是找到一个发射机位置，使其在场景几何条件下最大化无线观测的似然。候选发射机位置的似然由学习到的评分函数计算，该函数将观测到的时延-到达角（AoA）频谱与该候选位置对应的预测频谱进行匹配。分层场景编码器提取与传播相关的特征，以生成视距（LoS）和一次反射路径的几何先验。对于同步不完善的场景，我们进一步引入对飞行时间（ToF）鲁棒的GLocFM模型，以处理未知的ToF偏移。GLocFM在包含221个不同场景的多模态合成室内定位数据集上进行训练，该数据集的相关无线信号使用Sionna RT生成。在合成数据集和基于真实测量的NeRF²数据集上，GLocFM相对于某一最先进定位基线的平均三维定位误差分别降低了49.5%和48.8%。对不同接收机数量、带宽和阵列尺寸的消融实验进一步证明了所提框架的有效性和鲁棒性。
英文摘要
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.
Comments13 pages, single column
