Novel classification method to predict the accuracy of UWB ranging estimates

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

Methodology and key performance indicators (KPIs) for railway on-board positioning systems

The European Union (EU) is bolstering the railway sector with the aim of making it a direct competitor of the aviation sector. For that to occur, railway efficiency has to be improved by means of increasing capacity and reducing operational expenditure. Tracks are currently used below their maximum capacity. Given this fact and the EU's goals for the railway sector, research on solutions for on-board positioning system based on global navigation satellite systems (GNSS) have arisen in recent years. By taking advantage of GNSS, safety critical positioning systems will be able to use the infrastructure more efficiently. However, GNSS based positioning systems still cannot fulfill current normative validation processes, mainly, due to the fact that GNSS based positioning performance evaluation is not compatible with the key performance indicators (KPIs) used to assess railway systems performance: reliability, availability, maintainability, and safety. This paper proposes a methodology and unified key performance indicators (KPIs). Additionally, it shows real examples to address this issue. It aims to fill the gap between the current railway standardization process and any on-board positioning system