Radio based Cooperative Positioning for Vehicle-to-Vehicle Systems in Urban Scenarios

This master thesis aims at improving vehicle positioning in high-speed movement scenarios where global navigation satellite system (GNSS) does not work well. The standard 802.11p, as one of the WiFi family members, it can support to share vehicle information between vehicle and vehicle or between vehicle and infrastructure in a high-speed movement environment. Without the help of GNSS, the vehicles can estimate their position by sharing position information with other vehicles. In order to reach highly accurate positioning in urban scenarios, non-linear filters, such as extended Kalman filter (EKF), the square root of cubature Kalman filter (SCKF) and particle filters (PF) are investigated in this thesis. These filter algorithms are simulated in MATLAB to evaluate the positioning performance. Useful information from vehicles are used in the algorithms, such as velocity, acceleration of vehicles. To obtain realistic scenarios, vehicles are simulated in different road networks in SUMO and obtain the vehicle information from one another in GEMV2 and NS3. SUMO, GEMV2 and NS3 are the tools to help the simulation. In the simulations, the positioning accuracy is greatly improved when sharing vehicle information and utilizing the filter algorithm. This thesis compares the advantages and disadvantages of two filter algorithms. One is the square root of cubature Kalman filter, the other is particle filter. As a conclusion from the simulation result, the SCKF works better than the particle filter and it improves the accuracy of positioning when the GNSS is not well received. (Less)