Clustering optimization strategy for cooperative positioning system aided by UAV

For multi-vehicle networks, Cooperative Positioning (CP) technique has become a promising way to enhance vehicle positioning accuracy. Especially, the CP performance could be further improved by introducing Sensor-Rich Vehicles (SRVs) into CP networks, which is called SRV-aided CP. However, the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments, in which the sub-clusters with few SRVs will suffer from degradation of CP performance. Since Unmanned Aerial Vehicles (UAVs) have been widely used to aid vehicular communications, we intend to utilize UAVs to assist sub-clusters in CP. In this paper, a UAV-aided CP network is constructed to fully utilize information from SRVs. First, the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs. After that, the clustering optimization strategy is proposed, in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs, and then broadcasts this positioning-related information to other low-precision sub-clusters. Finally, the Locally-Centralized Factor Graph Optimization (LC-FGO) algorithm is designed to fuse positioning information from cooperators. Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.