# Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization

arXiv: https://arxiv.org/abs/2209.03021 · HTML: https://mlsanity.com/paper/2209.03021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [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, 1 of 1)

- [Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network](https://mlsanity.com/paper/2303.11725/) (2023)

## Abstract (arXiv)

Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology.