# Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning

arXiv: https://arxiv.org/abs/2403.19262 · HTML: https://mlsanity.com/paper/2403.19262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)

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

- [UWB TDoA Error Correction using Transformers: Patching and Positional Encoding Strategies](https://mlsanity.com/paper/2507.03523/) (2025)

## Abstract (arXiv)

Indoor positioning using UWB technology has gained interest due to its centimeter-level accuracy potential. However, multipath effects and non-line-of-sight conditions cause ranging errors between anchors and tags. Existing approaches for mitigating these ranging errors rely on collecting large labeled datasets, making them impractical for real-world deployments. This paper proposes a novel self-supervised deep reinforcement learning approach that does not require labeled ground truth data. A reinforcement learning agent uses the channel impulse response as a state and predicts corrections to minimize the error between corrected and estimated ranges. The agent learns, self-supervised, by iteratively improving corrections that are generated by combining the predictability of trajectories with filtering and smoothening. Experiments on real-world UWB measurements demonstrate comparable performance to state-of-the-art supervised methods, overcoming data dependency and lack of generalizability limitations. This makes self-supervised deep reinforcement learning a promising solution for practical and scalable UWB-ranging error correction.