# Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge

arXiv: https://arxiv.org/abs/2011.14684 · HTML: https://mlsanity.com/paper/2011.14684/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition](https://mlsanity.com/paper/2107.00606/) (2021)
- [Ultra Inertial Poser: Scalable Motion Capture and Tracking from Sparse Inertial Sensors and Ultra-Wideband Ranging](https://mlsanity.com/paper/2404.19541/) (2024)
- [Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation](https://mlsanity.com/paper/2209.03355/) (2022)
- [DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection](https://mlsanity.com/paper/2108.13157/) (2021)
- [A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform](https://mlsanity.com/paper/2305.18208/) (2023)
- [Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Ranging](https://mlsanity.com/paper/2510.21654/) (2025)
- [Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification](https://mlsanity.com/paper/2305.18206/) (2023)
- [Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization](https://mlsanity.com/paper/2209.03021/) (2022)
- [V2I Work Zone Geometry Reconstruction with Pose-Conditioned UWB Range Denoising](https://mlsanity.com/paper/2606.00119/) (2026)
- [Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network](https://mlsanity.com/paper/2303.11725/) (2023)
- [Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation](https://mlsanity.com/paper/2305.13904/) (2023)
- [Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction](https://mlsanity.com/paper/2607.05449/) (2026)

## Abstract (arXiv)

Ultra-wideband (UWB) is the state-of-the-art and most popular technology for wireless localization. Nevertheless, precise ranging and localization in non-line-of-sight (NLoS) conditions is still an open research topic. Indeed, multipath effects, reflections, refractions, and complexity of the indoor radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation at the edge. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions. Extensive experimentation with different settings and configurations has proved the effectiveness of our methodology and demonstrated the feasibility of a robust and low computational power UWB range error mitigation.