Research On GPS/BDS Vehicle Navigation Positioning And Path Planning

In recent years,with the development of technology,the target of target training has gradually transformed from the past fixed stationary target to a flexible mobile target.In combination with the development of the current training target,it is proposed to apply the mobile target car to military shooting.(1)Aiming to the problem of error during the positioning process,the error source is analyzed based on the principle of satellite navigation,and the error correction model is given,and the error is corrected.In response to issues of the GPS system and the BDS system and the coordinate system,the coordinate transformation method is used to unify the coordinate system of the BDS / GPS positioning system,and the time conversion method for the time system.(2)In order to improve the positioning accuracy of the combination system,this paper takes a pseudo-distance positioning method for a single system positioning,a traditional portfolio system positioning,and improvement combined system positioning.The improved combined system positioning has adopted a combined weight minimum multiplier and Kalman filter algorithm,analyzing traditional portfolios.The simulation results of the three methods of combined weighting least squares and the Kalman filtering algorithm have verified the optimized algorithm to effectively improve the positioning accuracy.(3)Combined with the environment in which the no-target car is located,the traditional Karman filtering algorithm is only suitable for linear systems,and uses an improved adaptive elsencing Kalman filter filtering algorithm,the algorithm utilizes a very large likelihood to slide windows The best estimate of the interval,that is,the new interest estimation covariance function verifies that the experiment verifies the effectiveness of the algorithm by collecting irregular route,and the results can be analyzed to improve the positioning of this algorithm in a complex environment.Accuracy.(4)For the target car ant colony algorithm,it is easy to fall into a local optimal solution when the global path planning is performed.This paper uses a global path planning algorithm based on improved ant colony algorithm.The method is modeled by the grid map.The parameters of the ant colony algorithm are optimized,and the ant foraging strategy is improved.According to the improved policy adjustment to the inspiration function,improve the information of the informationin update policy,and propose corresponding countermeasures for the U-shaped obstacles in the complex environment,the simulation results show that the improvement The algorithm can solve the problem of U-shaped obstacles and effectively shorten the length of the path planning.