Hybrid Cooperative Positioning for Vehicular Networks

Abstract

This paper proposes a hybrid cooperative positioning (CP) algorithm suitable for vehicular network applications which can fuse the measurements from global navigation satellites, ground stations, signals of opportunity, inter-node ranging from neighbouring vehicles and onboard inertial navigation systems (INS). By applying the framework of generalized approximate message passing (GAMP), the complex CP problem is transformed into an iterative yet lower computational load process. In each iteration, the time recurrence of navigation states and initialization of GAMP computation are conducted based on Kalman filter. The proposed algorithm guarantees the overall positioning performance of multiple vehicles in a hybrid navigation scenario, and improves the robustness and accuracy of CP navigation systems. Simulation results show that the proposed algorithm has better estimation accuracy than traditional CP algorithms, and has 20 times less computational load than the best existing algorithm with equivalent accuracy.