Abstract
A precise localization system is a key factor for several critical safety applications in Vehicular Ad Hoc Networks (VANets). Even though the Global Positioning System (GPS) can be used to provide the position estimation of vehicles, it still has an undesired error that can increase even more in some areas, making it unreliable and unfeasible for most critical safety applications. In this paper, we propose a new position estimation technique, named BOuND (acronym for Based ON Distance) localization, which can improve the GPS positions of nearby vehicles and minimize their errors based on the use of trigonometry concepts. Our solution uses an extended Kalman Filter to perform data fusion of both GPS and distance information among vehicles to provide an improved and precise position estimation. We evaluated the performance of our method using OMnet++ and SUMO. The obtained results clearly show that our proposal is capable of reducing the localization error in 53% that of GPS, and 37% when compared to the state-of-the-art VLOCI algorithm.