# A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform

arXiv: https://arxiv.org/abs/2305.18208 · HTML: https://mlsanity.com/paper/2305.18208/

## Key facts

- Cited by 5 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 23 May 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge](https://mlsanity.com/paper/2011.14684/) (2020)

## Built on by (most cited first, 5 of 5)

- [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)
- [UWB TDoA Error Correction using Transformers: Patching and Positional Encoding Strategies](https://mlsanity.com/paper/2507.03523/) (2025)
- [V2I Work Zone Geometry Reconstruction with Pose-Conditioned UWB Range Denoising](https://mlsanity.com/paper/2606.00119/) (2026)
- [Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction](https://mlsanity.com/paper/2607.05449/) (2026)
- [A Deep Generative Model for Synthesizing Labeled Wireless Signals](https://mlsanity.com/paper/2609.05396/) (2026)

## Abstract (arXiv)

Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation have shown great performance improvement via directly exploiting the wideband waveform instead of handcrafted features. However, these methods require data samples fully labeled with actual measurement errors for training, which leads to time-consuming data collection. In this paper, we propose a semi-supervised learning method based on variational Bayes for UWB ranging error mitigation. Combining deep learning techniques and statistic tools, our method can efficiently accumulate knowledge from both labeled and unlabeled data samples. Extensive experiments illustrate the effectiveness of the proposed method under different supervision rates, and the superiority compared to other fully supervised methods even at a low supervision rate.