From Least Squares to Deep Learning: Benchmarking Indoor Positioning on the HYMN Multi-Technology Dataset

Abstract

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.