Ultra-wideband high accuracy distance measurement based on hybrid compensation of temperature and distance error

Liu, Sheng; Yang, Hongze; Mei, Zhiqiang; Xu, Xiuying; He, Qianen

doi:10.1016/j.measurement.2022.112276

Cited by 7 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This classification model classifies received UWB ranging estimates as Good, Medium or Bad according to the ranging error magnitude. Usually, DW1000 transceivers obtain ranging errors under 15 cm in clear LOS conditions, as in the case of [29], [30]. Thus, measurements with an error of less than 15 cm are categorized as Good in this research work.…”

Section: A Proposed ML Classification Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Novel Classification Method to Predict the Accuracy of UWB Ranging Estimates

Arsuaga,

Ochoa-De-Eribe-Landaberea,

Zamora-Cadenas

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Real time location systems (RTLSs) are becoming more relevant in a more data driven economy and society due to their wide range of application cases. When the location of an object needs to be tracked with high accuracy, ultra wideband (UWB) technology is usually the best option. Nevertheless, UWB ranging estimates are not completely immune to some sources of error such as non line of sight (NLOS) or multipath conditions. Thus, this paper proposes a real-time classification model based on machine learning (ML) to predict if received ranging estimates are in line of sight (LOS) or NLOS conditions and discard those in NLOS. However, it is also shown that classifying measurements as LOS or NLOS does not guarantee detecting inaccurate ranging estimates, since LOS measurements can also yield large errors. As an example, the ranging root mean square error (RMSE) of the data labelled as LOS in a UWB based localization system database in the literature is of 0,714 m, significantly higher than the theoretical accuracy of a UWB system. Thus, a novel ML-based classification model is proposed to predict the magnitude of the ranging error. After applying the proposed classification model in the same data, the ranging RMSE of those ranging samples classified as most accurate is of only 0.183 m, significantly lower than the best RMSE we can obtain on the classical LOS/NLOS classification approach.INDEX TERMS DWM1000, machine learning, random forest, ranging errors, real time location system, ultra wideband.

show abstract

Section: A Proposed ML Classification Modelsmentioning

confidence: 99%

“…30 cm. In fact, it might also be possible to obtain errors of this magnitude under optimal conditions, as [30] obtained a maximum error of 28.5 cm. Thus, we will label ranging estimates with an error between 15 and 30 cm as Medium class.…”

Section: A Proposed ML Classification Modelsmentioning

confidence: 99%