Kalman filtering for improving car positioning with measurements of relative distance

Abstract

In this article, we propose a method for improving the estimates of the absolute positions of cars. In the proposed method, each car measures its velocity, the absolute position, and the relative distance to a car in front, and uploads these measurements to a server. Using these measurements, the server computes the estimates of the cars' positions by means of the Kalman filter. This method can estimate the absolute position even if the GPS receiver cannot receive enough signals from the satellites and cannot measure the absolute position. The experimental results show that the measurements of the relative distance suppress the uncertainties of the estimates.