In recent years, the interest in flying multiple Unmanned Aerial Vehicles (UAVs) in formation has increased. One challenging aspect of achieving this is the relative positioning within the swarm. This thesis evaluates two different methods for estimating the relative position and orientation between two fixed wing UAVs by fusing range measurements from Ultra-wideband (UWB) sensors and orientation estimates from Inertial Measurement Units (IMUs). To investigate the problem of estimating the relative position and orientation using range measurements, the performance of the UWB nodes regarding the accuracy of the measurements is evaluated. The resulting information is then used to develop a simulation environment where two fixed wing UAVs fly in formation. In this environment, the two estimation solutions are developed. The first solution to the estimation problem is based on the Extended Kalman Filter (EKF) and the second solution is based on Factor Graph Optimization (FGO). In addition to evaluating these methods, two additional areas of interest are investigated: the impact of varying the placement and number of UWB sensors, and if using additional sensors can lead to an increased accuracy of the estimates. To evaluate the EKF and the FGO solutions, multiple scenarios are simulated at different distances, with different amounts of changes in the relative position, and with different accuracies of the range measurements. The results from the simulations show that both solutions successfully estimate the relative position and orientation. The FGO-based solution performs better at estimating the relative position, while both algorithms perform similarly when estimating the relative orientation. However, both algorithms perform worse when exposed to more realistic range measurements. The thesis concludes that both solutions work well in simulation, where the Root Mean Square Error (RMSE) of the position estimates are 0.428 m and 0.275 m for the EKF and FGO solutions, respectively, and the RMSE of the orientation estimates are 0.016 radians and 0.013 radians respectively. However, to perform well on hardware, the accuracy of the UWB measurements must be increased. It is also concluded that by adding more sensors and by placing multiple UWB sensors on each UAV, the accuracy of the estimates can be improved. In simulation, the lowest RMSE is achieved by fusing barometer data from both UAVs in the FGO algorithm, resulting in an RMSE of 0.229 m for the estimated relative position.

This doctoral thesis presents a comprehensive exploration of ultrawideband technology in addressing diverse challenges within localization systems. Beginning with the development of an innovative, cost-effective, and anonymous contact tracing solution for industrial environments during the COVID-19 pandemic, the research integrates ultra-wideband positioning, Bluetooth low-energy, and inertial measurement units. The subsequent sections delve into relative positioning systems, device-free localization, UWB bistatic radar sensors, and UAV-based tracking, showcasing novel methodologies and hardware implementations with promising outcomes. The work extends to groundbreaking approaches in deploying UWB infrastructure through self-deployable robots and cooperative positioning schemes using a UAV swarm. The contributions highlight versatility, costeffectiveness, and scalability, opening new possibilities for applications in security, logistics, IoT services, and space exploration. In summary, this thesis represents a significant advancement in localization systems, offering practical solutions and paving the way for future research and applications

Trafikolyckor är ett problem i dagens samhälle och nya lösningar inom intelligenta transportsystem (ITS) efterfrågas. En lösning är fordon-till-fordon kommunikation (V2V), där säkrare trafik kan uppnås genom att fordon sänder relevant trafikdata till omgivande trafik. I denna rapport undersöktes och utvecklades metoder för en följdetekteringsalgoritm inom V2V kommunikation. Uppdraget var att hitta en lösning för följdetektering med GPS och radiokommunikation med FM-RDS, där hänsyn togs till tidsfördröjning. Algoritmer utvecklades i en simuleringsmiljö, för att sedan implementeras i en egenkonstruerad prototyp för mätning i trafik. För effektivt utnyttjande av FM-RDS-paket komprimerades den sända positionen, för att sedan rekonstrueras vid mottagarsidan.Resultaten av simuleringarna och trafikmätningarna visade att det var möjligt att detektera om ett fordon följde efter ett annat i trafiken, med hjälp av GPS-positionering och kommunikation genom FM-RDS med tidsfördröjning av mottagna paket. Vid högre tidsfördröjningar så presterade algoritmen sämre, dock finns det potential till förbättring. Förslag på förbättring av rekonstruktionsfunktionen samt algoritmen behandlas i denna rapport. / Traffic accidents are a problem in today’s society and new solutions regarding intelligent transportation systems (ITS) are being requested. One solution to such is vehicle-to-vehicle communication (V2V), where safer traffic can be achieved by vehicles transmitting relevant traffic information to surrounding vehicles. In this report, we researched and developed methods for a follow-detection algorithm for V2V communication. The task was to find a solution for follow-detection by using GPS and FM-RDS, with regards to time delay. An algorithm was developed in a simulated environment, to then be implemented in a prototype for measuring traffic. For efficient usage of FM-RDS packets, the transmitted position was compressed and then decompressed at the receiving side. The results of the simulations and traffic tests indicated that it was possible to detect if a vehicle followed another in traffic, by using GPS positioning and FM-RDS with time delay. The accuracy of the algorithm degraded at higher time delays, however there is potential for improvements. Suggestions for improvements of the reconstruction function and the algorithm are discussed in this report.