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

TL;DR

This paper presents a low-power deep learning approach to mitigate range errors in UWB localization, significantly improving accuracy in NLOS and LOS conditions with minimal energy consumption.

Contribution

It introduces a novel deep neural network-based error mitigation method optimized for ultra-low-power microcontrollers for UWB localization.

Findings

Effective correction of NLOS and LOS range errors.

Low power consumption of a few milliwatts.

Enhanced localization accuracy in practical environments.

Abstract

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.