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

Key points

• 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.1
• 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.1
• We propose ChronoChart, a timestamp-supervised CC framework that requires only CSI and timestamps without prior knowledge for training.1
• 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.1

Sources (1)

• [1]ChronoChart: Setup-Agnostic Channel Charting for Label-Free Indoor Localization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 11:40 AM

  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.

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