# Error Mitigation for TDoA UWB Indoor Localization using Unsupervised Machine Learning

arXiv: https://arxiv.org/abs/2404.06824 · HTML: https://mlsanity.com/paper/2404.06824/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)

## 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 systems based on Ultra-wideband (UWB) technology are gaining recognition for their ability to provide cm-level localization accuracy. However, these systems often encounter challenges caused by dense multi-path fading, leading to positioning errors. To address this issue, in this letter, we propose a novel methodology for unsupervised anchor node selection using deep embedded clustering (DEC). Our approach uses an Auto Encoder (AE) before clustering, thereby better separating UWB features into separable clusters of UWB input signals. We furthermore investigate how to rank these clusters based on their cluster quality, allowing us to remove untrustworthy signals. Experimental results show the efficiency of our proposed method, demonstrating a significant 23.1% reduction in mean absolute error (MAE) compared to without anchor exclusion. Especially in the dense multi-path area, our algorithm achieves even more significant enhancements, reducing the MAE by 26.6% and the 95th percentile error by 49.3% compared to without anchor exclusion.