从最小二乘到深度学习：基于HYMN多技术数据集的室内定位基准测试
From Least Squares to Deep Learning: Benchmarking Indoor Positioning on the HYMN Multi-Technology Dataset
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中文总结 AI 辅助
本研究在HYMN数据集上系统比较了从最小二乘到深度学习的四种室内定位方法，发现网格滤波器在融合多技术测距时与深度学习性能相当，且揭示了空间泛化惩罚问题。
中文摘要 AI 辅助
室内定位基准测试很少将多种技术与高精度地面真值共同部署，这限制了跨方法和跨技术的比较。我们在一个工业设施中记录的48个参考点上，对融合的超宽带、蓝牙低功耗和WiFi测距数据评估了四种基于测距的定位方法，并提供了地面真值。所评估的方法从最小二乘定位（带和不带鲁棒加权）到贝叶斯网格滤波器，再到我们报告了两种协议（插值和空间泛化）下的ResNet回归器。各技术的测距质量大致跨越两个数量级，这限制了任何以等价方式加权锚点的基于几何的求解器。在融合输入上，网格滤波器在中位误差方面与深度学习相当，而ResNet的优势集中在上尾部分。将参考点排除在训练之外会使学习回归器的中位误差增加数倍，暴露出在随机分割下不可见的空间泛化惩罚。数据集和评估代码公开可用。
英文摘要
Indoor positioning benchmarks rarely co-locate multiple technologies with high-accuracy ground truth, limiting cross-method and cross-technology comparison. We evaluate four range-based positioning methods on fused Ultra-wideband, Bluetooth Low Energy, and WiFi ranges recorded at 48 reference points in an industrial facility with available ground truth. The evaluated methods range from least-squares positioning, with and without robust weighting, through a Bayesian grid filter to a ResNet regressor that we report under two protocols, interpolation and spatial generalisation. Per-technology ranging quality spans roughly two orders of magnitude, which caps any geometry-based solver that weighs anchors equivalently. On fused input the grid filter is competitive with deep learning on median error, while the ResNet's advantage concentrates on the upper tail. Holding reference points out of training increases the learned regressor's median error several-fold, exposing a spatial-generalisation penalty invisible under random splits. The dataset and evaluation code are publicly available.
发表机构
- TUD Dresden University of Technology（德累斯顿工业大学）
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