ChronoChart: Setup-Agnostic Channel Charting for Label-Free Indoor Localization

Abstract

Accurate radio-based indoor localization remains challenging in multipath-rich and dynamic indoor environments. Supervised fingerprinting methods achieve high positioning accuracy by learning mappings from radio channel measurements to physical locations, but require costly position labels and frequent recalibration. Channel Charting (CC) addresses this limitation by learning low-dimensional representations of Channel State Information (CSI) without position labels, yielding channel charts in which nearby points correspond to nearby physical locations. However, prominent CC methods rely on setup-specific dissimilarity metrics, such as ADP-, CIRA-, or PDR-based distances, and on precomputed $O(n^2)$ distance matrices, limiting scalability and transferability across radio systems. We propose ChronoChart, a timestamp-supervised CC framework that requires only CSI and timestamps without prior knowledge for training. Our method jointly optimizes complementary objectives: a triplet loss using temporally derived positive and negative samples to establish coarse global structure, a temporal-proximity loss that promotes smooth trajectories in the chart, and a joint-embedding loss that enforces consistency between representations of different augmentations. Evaluated on real-world 5G, MIMO, and passive UWB data, our method matches or exceeds prior CC baselines that depend on tailored distance metrics, and remains scalable and applicable across heterogeneous radio systems.