Proposed Technique to Improve VANET’s Vehicle Localization Accuracy in Multipath Environment

Abstract

Localization (location estimation) of a vehicle in Vehicular Ad-hoc Network (VANET) has been studied in many fields since it has the ability to provide a variety of services like navigation, vehicle tracking and collision detection etc. Global Positioning System (GPS) and Inertial Navigation System (INS) both are very useful method of localization. By using Kalman Filter it is possible to combine these two systems to get better accuracy of localization. Now day’s typical localization techniques combines GPS receiver measurement and measurements of the vehicle’s motion by INS. However, when the vehicle traveling through an environment that creates a multipath effect, these techniques fail to produce the high localization accuracy that they attain in an open environments because of loss of satellite signal in a multipath area, such as areas with high buildings, trees, or tunnels. In this new advance localization technique is proposed to improve localization accuracy. Also Artificial Neural Network is used to detect multipath environment and then by using Nelder Mead Optimization method we can reduce the localization error of a vehicle when it travelling through multipath environment