可解释的物理信息WiFi室内定位：学习有效的接入点几何结构并用于剪枝
Interpretable Physics Informed WiFi Indoor Localization: Learning an Effective Access Point Geometry and Using It to Prune
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中文总结 AI 辅助
提出物理信息驱动的分层深度学习框架，联合预测位置并学习AP几何以剪枝，在UJIIndoorLoc上降低误差26%-36%，并支持无坐标场景下的有效特征选择。
中文摘要 AI 辅助
深度学习模型在室内定位中可以实现高精度，但其黑盒特性限制了可解释性和所学信息的重用。我们提出了一种基于WiFi指纹的室内定位分层深度学习框架，该框架联合预测用户位置并学习周围接入点（APs）的有效几何结构。物理信息解码器直接从RSSI测量值和带标签的用户位置推断该几何结构，训练过程中无需真实的AP坐标。学习到的几何结构随后用于对AP进行排序和剪枝。在UJIIndoorLoc数据集上，所提出的链式模型实现了7.07米的平均3D定位误差，与基线模型相比误差降低了26%至36%。先前在相同官方划分上评估的已发表方法报告的误差高出10.6%至31.0%。剪除35%或50%的AP仅导致定位精度的微小损失。推断的几何结构还使得基于Fisher信息的AP排序成为可能，即使指纹数据库不包含测量的AP坐标。在Tampere/TUT和UTSIndoorLoc数据集上的实验表明，几何引导的AP选择与直接基于标记数据构建的选择器性能相当。这些结果表明，物理信息的可解释性可以改善室内定位，同时支持有效的特征选择。
英文摘要
Deep learning models can achieve high accuracy for indoor localization, but their black-box nature limits interpretability and the reuse of learned information. We propose a hierarchical deep learning framework for WiFi fingerprint-based indoor localization that jointly predicts user location and learns an effective geometry of the surrounding access points (APs). Physics-informed decoders infer this geometry directly from RSSI measurements and labelled user positions, without requiring the true AP coordinates during training. The learned geometry is then used to rank and prune APs. On the UJIIndoorLoc dataset, the proposed chained model achieves a mean 3D localization error of 7.07 m, reducing error by 26% to 36% compared with baseline models. Previously published methods evaluated on the same official split report errors 10.6% to 31.0% higher. Pruning 35% or 50% of the APs causes only a small loss in localization accuracy. The inferred geometry also enables Fisher-information-based AP ranking even when fingerprint databases do not contain surveyed AP coordinates. Experiments on the Tampere/TUT and UTSIndoorLoc datasets show that geometry-guided AP selection performs comparably to selectors built directly from labelled data. These results show that physics-informed interpretability can improve indoor localization while also supporting effective feature selection.
