A DSRC-Based Vehicular Positioning Enhancement Using a Distributed Multiple-Model Kalman Filter

Some inherent shortcomings of the global positioning systems (GPSs), such as limited accuracy and availability, limit the positioning performance of a vehicular location system in urban harsh environments. This motivates the development of cooperative positioning (CP) methods based on emerging vehicle-to-anything communications. In this paper, we present a framework of vehicular positioning enhancement based on dedicated short range communications (DSRC). An interactive multiple model is first used to track the distributed manners of both the vehicle acceleration variations and the switching of the covariances of DSRC physical measurements such as the Doppler frequency shift and the received signal strength indicator, with which a novel CP enhancement method is presented to improve the distributed estimation performance by sharing the motion states and the physical measurements among local vehicles through vehicular DSRC. We have also presented an analysis on the positioning performance, and a closed-formed lower bound, named the modified square position error bound (mSPEB), is derived for bounding the positioning estimation performance of CP systems. Simulation results have been supplemented to compare our proposed method with the stand-alone GPS implementation in terms of the root-mean-square error (RMSE), showing that the obtained positioning enhancement can improve comprehensive positioning performance by the percentage varying between about 35% and about 72% under different traffic intensities and the connected vehicle penetrations. More importantly, the RMSE achieved by our method is shown remarkably closed to the root of the theoretical mSPEB.