Research On High-precision Signal Localization Technology Based On Vehicle-vehicle Cooperation

In recent years,with the development of 5G mobile communication system and the progress of vehicle networking technology,using signal to locate intelligent connected vehicles has become a research hotspot.However,the current positioning methods require a large number of base stations,and the accuracy needs to be improved.In order to solve the above problems,starting from reducing the use of base stations and improving the positioning accuracy,this thesis studies the estimation of multipath signal parameters and vehicle cooperative positioning respectively,and realizes the high-precision signal positioning based on vehicle-vehicle cooperation of a single base station.In terms of parameter estimation,in order to reduce the use of base stations by using multipath signals,this thesis proposes a downlink multipath signal estimation algorithm,which estimates the arrival time and angle of the downlink signal matching each path received by the vehicle.Firstly,the arrival time of each path is estimated in time domain by using the strong correlation characteristic of downlink positioning reference signal.Secondly,using the signal of each subcarrier on each receiving antenna to extend antenna array,and designing a joint estimation algorithm of multipath arrival time and angle.Finally,we use MATLAB toolboxs to build a simulation system to evaluate the performance of the algorithm.The results show that the parameter estimation algorithm can break through the limitation of the number of antennas and estimate the parameters of multipath signals.On the basis of multipath signal parameter estimation,a vehicle-vehicle communication-assisted cooperative location algorithm is proposed in this thesis for the location problem of connected vehicles.The algorithm uses the location-related information in the vehicle-vehicle communication and the multipath signals received by the vehicle to further improve the localization accuracy.Firstly,the scene is modeled for signal multipath propagation and vehicle-vehicle communication.Secondly,in order to realize feature-based synchronous location and mapping,a systematic Bayesian model is established according to the characteristics of information exchange between vehicles and multipath components.Finally,a cooperative localization algorithm based on belief propagation is designed to calculate the location of vehicles through factor graph.In this paper,simulation experiments are conducted for particle convergence speed,vehicle trajectory tracking and localization accuracy.The simulation results show that the algorithm has better localization performance than the previously proposed localization method BP-SLAM.