UWB/GNSS-based cooperative positioning method for V2X applications

Limited availability of GNSS signals in urban canyons is a challenge for the implementation of many positioning-based traffic safety applications, and V2X technology provides an alternative solution to resolve this problem. As a key communication component in V2X technology, Dedicated Short Range Communication (DSRC) not only allows vehicles to exchange their position, but also traffic safety related information such as real-time congestion, up-to-date accident details, speed limits, etc. This position and traffic information could underpin various traffic safety applications - for instance, lane departure warnings, potential collision avoidance, and traffic congestion warnings. By taking advantage of DSRC, a vehicle in a GNSS denied environment is able to calculate its position using the assistance of other vehicles with sufficient GNSS signals to fix their locations. The concept of cooperative positioning, which is also called collaborative positioning, has been proposed to achieve this goal

Hybrid and Cooperative Positioning Solutions for Wireless Networks

In this thesis, some hybrid and cooperative solutions are proposed and analyzed to locate the user in challenged scenarios, with the aim to overcome the limits of positioning systems based on single technology. The proposed approaches add hybrid and cooperative features to some conventional position estimation techniques like Kalman filter and particle filter, and fuse information from different radio frequency technologies. The concept of cooperative positioning is enhanced with hybrid technologies, in order to further increase the positioning accuracy and availability. In particular, wireless sensor networks and radio frequency identification technology are used together to enhance the collected data with position information. Terrestrial ranging techniques (i.e., ultra-wide band technology) are employed to assist the satellite-based localization in urban canyons and indoors. Moreover, some advanced positioning algorithms, such as energy efficient, cognitive tracking and non-line-of-sight identification, are studied to satisfy the different positioning requirements in harsh indoor environments. The proposed hybrid and cooperative solutions are tested and verified by first Monte Carlo simulations then real experiments. The obtained results demonstrate that the proposed solutions can increase the robustness (positioning accuracy and availability) of the current localization system