Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization
This addresses localization accuracy issues for applications using UWB technology, but it is incremental as it applies existing optimization techniques to a known bottleneck.
The paper tackled the problem of inaccurate UWB localization due to NLOS conditions by introducing a deep neural network-based range error mitigation solution, achieving corrections with only a few mW of power.
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.