Feasibility Analysis of Positioning and Navigation Strategies for Railway and Tramway Applications

Abstract

Abstract The problem of vehicle autonomous driving currently represents a topic of great interest from both theoretical and practical points of view. Among the challenging tasks to be addressed within any autonomous driving framework, one of the most important ones is localization from data collected in real time. Within such framework, this paper is specifically focused on the localization problem for rail vehicles, such as railway and tramway vehicles. Our specific interest is on investigating solutions to the localization problem which are (as much as possible) independent on ground sensor infrastructure and are therefore suitable to be employed on any rail vehicle, irrespective of the ground equipment of the specific tracks. To this end, we refer to a multi-sensor framework and, specifically, to a sensor fusion scheme which collects data from different sensors installed on the vehicle (namely, an Inertial Measurement Unit and a Global Positioning System) and carries out a Kalman-based filtering recursion which relies on a simplified vehicle model. With the aim of identifying a solution for the localization problem providing desirable performance, we carry out a comparative simulation analysis concerning different Kalman-based data fusion strategies (in particular, the Extended Kalman Filter and the Unscented Kalman Filter are considered).