Multi-vehicle cooperative positioning based on edge-computed multidimensional scaling

Abstract

As the basis of location-based services (LBS), positioning is one of the most essential parts in intelligent transportation systems (ITS). Although global positioning system (GPS) has been widely used in vehicle positioning, it can not achieve lane level positioning accuracy. Motivated by the mature ranging technologies such as radar and ultra-wideband (UWB), several cooperative positioning (CP) methods have been proposed to enhance the accuracy and robustness of GPS. In this paper, we proposed a two-stage CP algorithm that combines multidimensional scaling (MDS) and Procrustes analysis for vehicles with GPS information. Specifically, the optimized MDS based on the scaling by majorizing a complicated function (SMACOF) algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions, then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information. All the computations are performed at the mobile edge computing node (MECN) for the request of ultra-reliable and low latency communications (URLLC). Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.