Breaking the Gridlock of Spatial Correlations in GPS-Aided IEEE 802.11p-Based Cooperative Positioning
Spatial correlations found in vehicular mobility are jeopardizing the precision level of cooperative positioning for future Cooperative Intelligent Transport System (C-ITS) applications. Bayesian filters traditionally assume independence of the measurement noise terms over space between different vehicles and over time at each vehicle, whereas they are actually correlated due to the local continuity of physical propagation phenomena (e.g., shadowing, multipath, etc.) under highly constrained vehicular mobility. In this paper, we break this gridlock by proposing an innovative data fusion framework capable of mitigating these effects to maintain the positioning precision level under severely correlated environments. We first illustrate the dramatic impact of correlated noise affecting both global positioning system and vehicle-to-vehicle (V2V) received power observations. Then, we propose a new generic data fusion framework based on a particle filter supporting three complementary methods to decorrelate measurement noises in a globally asynchronous context. Compared with conventional cooperative positioning, simulations performed in canonical vehicular scenarios (highway, urban canyon, tunnel) show that our proposed approach could provide up to 60% precision improvement in correlated environments, while matching by less than 15-20% deviation, an optimal cooperative positioning scheme considered under independent measurements.