UWB Positioning Integrity Estimation Using Ranging Residuals and ML Augmented Filtering

This article investigates the use of ultrawideband (UWB) ranging residuals for coordinate integrity estimation and their use in a filtering scheme. Typically, UWB system accuracy is improved using channel statistics (CSs) to detect and mitigate non-line-of-sight effects between UWB sensors and the object to be located, potentially improving the end coordinate solution. However, in practice, when considering UWB system with a high positioning update rate, this is not a feasible approach, as gathering and processing CS data takes too much time. In contrast to this approach, this article proposes a set of features based on UWB ranging residuals that could be used as an alternative in integrity assessment. By using machine learning (ML), the most important features were extracted from the initial set, and then, used to train and validate a model for UWB coordinate error prediction. Finally, the prediction was applied in an adaptive Kalman filtering scheme as an input for measurement uncertainty. Model testing was done using UWB measurement test dataset gathered at an industrial site. The overall results showed significant improvement in 2-D and 3-D positioning metrics of ML-augmented filtering when compared to non-ML filtering. On average, the end coordinates in the test set had ca. 10 cm smaller mean location error and ca. 40 cm smaller dispersion in 2-D positioning. In addition, the presence of outliers was reduced significantly as the maximum error offset decreased by several meters. Although ML augmented filtering is computationally slower than non-ML filtering (e.g., ordinary and extended Kalman filter), it is still faster than using CS for UWB integrity estimation. The results show that using the proposed residual features in an ML model provides a feasible approach to predict UWB positioning integrity and use it as a measure of uncertainty in a coordinate filtering scheme.