Autonomous vehicle positioning with GPS in urban canyon environments

Abstract

The positioning systems based on GPS alone face great problems in the so called urban canyon environments where the GPS signals are often blocked by high buildings and there are not enough available satellites signals to estimate the positioning information of a fix. To solve the problem, a constrained method is presented by approximately modeling the path of the vehicle in the urban canyon environments as pieces of lines. By adding this constraint, the minimum number of available satellites reduces to two, which is satisfied in many urban canyon environments. Then, a state-augmentation method is proposed and the extended Kalman filtering technique is adopted to simultaneously estimate the positions of the GPS receiver and the parameters of the line. Simulation results show that this approach can solve the urban canyon problems successfully.