Self-calibration and Collaborative Localization for UWB Positioning Systems

Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

Integrating collaborative localization into professional translator training

Non-professional translation (NPT) has attracted increasing attention in translation studies in recent years. As a consequence, translator training needs to take NPT into account in the translation curriculum. In this article I report the findings of an exploratory study conducted to implement and evaluate an online collaborative localization project as an example of NPT. Drawing on quantitative and qualitative methods, the findings show that the trainees had a positive attitude towards NPT practices and were highly satisfied with the inclusion of NPT in the localization curriculum.