Control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation

This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou navigation. While navigating in urban canyons, the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou signal. Here, the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system, and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback control. The proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system, without the full knowledge of the vehicle dynamics. It is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate. A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.