Research On The Blind Area Coordinated Positioning Algorithm Of Car Networking Based On Wireless Ranging

As an outstanding part of the Internet of Things,the Internet of Vehicles has gradually attracted people's attention.The vigorous development of Internet of Vehicles is conducive to achieving intelligent transportation,reducing road congestion and traffic accidents.Location is a significant part of intelligent transportation.At present,Global Navigation Satellite System is widely used in vehicle positioning all over the world.However,if GNSS signals are not available,the cooperative location of vehicles based on wireless ranging is one of the effective methods to realize the location of Internet of Vehicles.In this thesis,some useful explorations are made on localization methods of Internet of Vehicles in the blind area.Three types of node localization methods suitable for dead zones of Internet of Vehicles are studied.Firstly,a relative positioning method based on Road-Side Units and wireless ranging is proposed to correct INS positioning information.A mathematical model of vehicle-road coordinated positioning is established and solved by weighted non-linear least squares(WNLS)algorithm,so that the inertial navigation positioning error is effectively delayed in the simulation experiment based on highway environment.Secondly,when there is no vehicle inertial navigation system,an iterative extended Kalman filter(IEKF)algorithm is designed to take the state of the system into account,which effectively improves the vehicle tracking effect.Thirdly,aiming at the complex areas such as urban blocks with many buildings where large ranging errors may occur in,a cooperative localization algorithm based on LSSVR-W for vehicular networking is constructed.This algorithm can still maintain a good localization effect in the case of large ranging errors in both 2D and 3D environment.The simulation analysis shows,in the simple case with less disturbance,the WNLS algorithm and IEKF algorithm have good performance on vehicles positioning,fast calculation speed and low power consumption.In the complex environment with large ranging error,the LSSVR-W algorithm has more advantages and environmental adaptability.