Two stage integration of GPS, kinematic information, and cooperative awareness messages using cascaded kalman filters

Abstract

Availability of accurate and continuous positioning information is a fundamental requirement for Vehicular Ad Hoc Networks. However, existing positioning approaches does not fulfill the required accuracy of many VANET’s applications and services. Integrating GPS with vehicles’ kinematic information (GPS/DR) is widely suggested for vehicular positioning. In many cases where the GPS is unavailable or instable for long time, this integration resulted in inaccurate positioning. Recently, cooperative positioning (CP) based on vehicle-to-vehicle (V2V) communication have been proposed as an alternative for GPS/DR in many ad hoc networks. Even though, CP needs high communication to achieve the required accuracy, which is not guaranteed in VANET harsh environment. In this paper, two-stage integration algorithm is proposed to ensure continuous and accurate positioning information. The proposed algorithm integrates GPS and kinematic sensors measurements, as well as neighboring mobility information based on two cascaded Kalman filters (KF). The first KF is used to integrate GPS information with kinematic sensors measurements (dead reckoning). Meanwhile, the second KF is used to integrate the results of the first KF with the neighboring mobility information. Results show that the proposed algorithm outperformed the conventional integration algorithm in terms of positioning accuracy under the tested scenario.