Research On Location Technology Of Interested Vehicle Based On VANET

Recently, Vehicle ad hoc network(VANET) plays out a great research value and application capabilities in both the highway traffic environment and the urban traffic congestion. It is a self-organized and open inter-vehicle communication network, and now is widely used in vehicle tracking, traffic control, road testing and other areas. The VANET network routing protocols need to meet the requirements of high-speed vehicle, frequent-change topology and a large number of vehicles node. In addition, in order to increase the localization accuracy, data integration of different vehicles and sensors is an important way, which is expected to improving the safety and effectiveness of different driving environments. Due to the low accuracy and limitations of single sensor measurement, a multi-vehicle multi-sensor fusion algorithm, Kalman, is presented in this paper. After modeling and simulation, the given algorithm is proved to be much accurate than traditional one.Firstly,this paper introduces the VANET and localization, describes the characteristics, technology, routing protocols of VANET, gives localization methods and the algorithm application scenario model, discusses the localization algorithm performance and its influence by network parameters, and presents infrared image processing, several images ranging technology and its performance.Secondly, this paper introduces an improved vehicle targeting algorithm, Kalman Multi-vehicle fusion algorithm. It is based on both Kalman filter fusion algorithm, which uses signal vehicle GPS and image ranging, and Kalman between several vehicles. This combined algorithm improve the localization accuracy under the VANET. Based on ranging and error analysis, a mathematic model is given, and with suitable hypothesis of network performance, several simulations completed by MATLAB is given. These simulations contain the proposed algorithm and the signal vehicle Kalman filter fusion algorithm. The simulation results show the proposed algorithm improves the localization accuracy.Finally, a brief summary of the paper, the problem may arise in next VANET target localization, and the solutions are given.