# UWB TDoA Error Correction using Transformers: Patching and Positional Encoding Strategies

arXiv: https://arxiv.org/abs/2507.03523 · HTML: https://mlsanity.com/paper/2507.03523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Overview of Ultra-WideBand (UWB) Standards and Organizations (IEEE 802.15.4, FiRa, Apple): Interoperability Aspects and Future Research Directions](https://mlsanity.com/paper/2202.02190/) (2022)
- [A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform](https://mlsanity.com/paper/2305.18208/) (2023)
- [High-Accuracy Ranging and Localization with Ultra-Wideband Communications for Energy-Constrained Devices](https://mlsanity.com/paper/2104.11042/) (2021)
- [5G Positioning Advancements with AI/ML](https://mlsanity.com/paper/2401.02427/) (2023)
- [Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System](https://mlsanity.com/paper/2108.10210/) (2021)
- [Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning](https://mlsanity.com/paper/2403.19262/) (2024)
- [Error Mitigation for TDoA UWB Indoor Localization using Unsupervised Machine Learning](https://mlsanity.com/paper/2404.06824/) (2024)

## Built on by (most cited first, 1 of 1)

- [Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction](https://mlsanity.com/paper/2607.05449/) (2026)

## Abstract (arXiv)

Despite their high accuracy, UWB-based localization systems suffer inaccuracies when deployed in industrial locations with many obstacles due to multipath effects and non-line-of-sight (NLOS) conditions. In such environments, current error mitigation approaches for time difference of arrival (TDoA) localization typically exclude NLOS links. However, this exclusion approach leads to geometric dilution of precision problems and this approach is infeasible when the majority of links are NLOS. To address these limitations, we propose a transformer-based TDoA position correction method that uses raw channel impulse responses (CIRs) from all available anchor nodes to compute position corrections. We introduce different CIR ordering, patching and positional encoding strategies for the transformer, and analyze each proposed technique's scalability and performance gains. Based on experiments on real-world UWB measurements, our approach can provide accuracies of up to 0.39 m in a complex environment consisting of (almost) only NLOS signals, which is an improvement of 73.6 % compared to the TDoA baseline.