This work presents a novel landing assistance system (LAS) capable of locating a drone for a safe landing after its inspection mission. The location of the drone is achieved by a fusion of ultra-wideband (UWB), inertial measurement unit (IMU) and magnetometer data. Unlike other typical landing assistance systems, the UWB fixed sensors are placed around a 2 x 2 m landing platform and two tags are attached to the drone. Since this type of set-up is suboptimal for UWB location systems, a new positioning algorithm is proposed for a correct performance. First, an extended Kalman filter (EKF) algorithm is used to calculate the position of each tag, and then both positions are combined for a more accurate and robust localisation. As a result, the obtained positioning errors can be reduced by 50% compared to a typical UWB-based landing assistance system. Moreover, due to the small demand of space, the proposed landing assistance system can be used almost anywhere and is deployed easily.

Automated Guided Vehicles (AGV) are unmanned transport vehicles widely used in the industrial sector to substitute manned industrial trucks and conveyors. In order to guarantee safe operation, AGVs must be equipped with a safety system to stop their movement in presence of obstacles or humans in their path. This work presents a novel safety system for AGVs that is based on Ultra Wideband (UWB) technology. Unlike previous works based on UWB Real-Time Location Systems (RTLS), the proposed safety system does not require installing hardware on the plant's infrastructure. Instead, the AGV is equipped with sensors capable of locating the tag of a person or a mobile asset. This simplifies deployment of the solution and enables its use in dynamic environments. The proposed safety system was mounted in an AGV designed by the company ASTI Mobile Robotics. Dynamic measurements showed that the proposed safety system accurately mirrors the relative movement between the AGV and tag. Furthermore, the proposed safety system employs a novel method for post-processing ranging data. Measurements showed that this method improves the accuracy of the system, resulting in a more homogeneously distributed positioning error around a room.

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

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.

Purpose This article focuses on a novel Advanced Train LocAtion Simulator (ATLAS) for on-board railway location using wireless communication technologies, such as satellite navigation and location based systems. ATLAS allows the creation of multiple simulation environments providing a versatile tool for testing and assessing new train location services. This enhancement reduces the number of tests performed in real scenarios and trains, reducing the cost and development time of new location systems as well as assessing the performance level for given tracks. Methods The simulation platform is based on modular blocks, where each block can be replaced or improved. The platform uses Monte Carlo Simulation to generate results with statistical significance. This implementation allows the modification of the development platform to cover multiple requirements, such as, ranging errors in the input parameters or including other positioning technologies. In this paper, the generated input parameter errors have been taken from the results of the field tests realized by the 3GPP ensuring the validity of the used parameter errors. However, these could be easily adapted by the user to particular characterized environments. Results Case studies for the validation of ATLAS will be also introduced, including preliminary results related to the use of Global System for Mobile communications in Railway (GSM-R) and Universal Mobile Telecommunications System (UMTS) technologies for positioning. The validation stage provides a way to test the platform functionalities and verify its flexibility. Conclusions The versatility of the platform to perform simulations using same configuration parameters for different case studies can be highlighted. Furthermore, first conclusions are drawn from the obtained results. The characterization of the infrastructure for the simulation and the performance improvement of the location systems in the tunnels (e.g., by including Inertial Measurement Unit (IMU)) are necessary to achieve accuracy levels that can be valid for ETCS level 3.