Research Of SINS And DVL Integrated Navigation System

Due to the working principle of the strapdown inertial navigation system,the speed information and the position information output by the navigation system are obtained through integral operation,which leads to the position data diverging due to the accumulation of the speed error of the long-time working navigation system,Doppler speedometer relatively high accuracy,can be used to modify the strapdown inertial navigation system speed information to improve the positioning accuracy.Therefore,a combined navigation system consisting of satellite-assisted Doppler navigation with strap-down inertial navigation system is adopted.The paper introduces the working principle of SINS and Doppler navigation,and establishes the error model of SINS and Doppler navigation.Then it introduced the basic theory of Kalman filter and particle filter,and built the filter model of integrated navigation system.Aiming at the problem of reduced positioning accuracy of integrated navigation system over time,an output correction scheme of indirect filtering model is proposed.Finally,the particle filter designed by Matlab / simulink is simulated.The paper gives the effect analysis of particle filter,and simulates the correction effect of particle filter on the output navigation parameters in the integrated navigation system.Compared with the Kalman filter,the paper discusses the problems existing in the controller.From the simulation results,it can be seen that the standard particle filter can accomplish the navigation well in the navigation tasks with short navigation time.When the navigation time is long,the particle filter will affect its filtering performance due to the particle degradation.Aiming at the problem of particle degeneration,we discuss many ways to improve the particle filter algorithm.The filtering performance of the improved particle filter algorithm has been significantly improved.At the end of the dissertation,the improved particle filter algorithm is validated for a long time,which proves that the performance of the improved particle filter algorithm is available.