Multisource-multitarget cooperative positioning using probability hypothesis density filter in internet of vehicles

Abstract

Accurate positioning of intelligent connected vehicle (ICV) is a key element for the development of cooperative intelligent transportation system. In vehicular networks, lots of state-related measurements, especially the mutual measurements between ICVs, are shared. It is an advisable strategy to fuse these measurements for a more robust positioning. In this context, an innovative framework, referred to as multisource-multitarget cooperative positioning (MMCP) is presented. In MMCP, ICVs are local information source, that upload both the states of ICVs estimated by on-board sensors and the relative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs are selected as the global targets, and the relative vectors are converted into global measurements. Then, the MMCP is modelled into a multi-target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM-PHD-LC) filter to match and fuse the global measurements to further improve the estimation of ICVs. The evaluation results show that our GM-PHD-LC can provide 10 Hz positioning services in urban area, and significantly improve the positioning accuracy compared to the standalone global navigation satellite system.