The Research On The Algorithm Of Integrated Navigation And Filtering For UAV

With the rapid development of information technology and consumer electronics industry giants,unmanned aerial vehicle(uav)has a wide application in various fields of information age and plays a very key role.In order to make the uav can operate in accordance with the scheduled orbit,the navigation control system is required to estimate the parameters of the navigation control system to meet the needs of its precision in the design stage of the control strategy.Navigation and positioning technology is the key technology to realize the flight,especially the uav flight task.However,any single navigation system can not meet the needs of the task,so it is usually used in the integrated navigation system.Filtering technology is the key data fusion technology which can make the integrated navigation system work well.Especially since the production of various types of derivative algorithm,it has provided a theoretical support for the integrated navigation system,which has great theoretical significance and practical application value.This thesis is based on the research of the integrated navigation filter algorithm,the main work includes the following aspects:1.The unmanned aerial vehicle(uav)was introduced in detail the development history and characteristics of each stage,and a variety of common kinds of integrated navigation are introduced.The working principle and the advantages and disadvantages of GPS/INS integrated navigation are analyzed in detail.2.The principle of strapdown inertial navigation and global positioning system is described in this paper.Then,the error sources of the system are briefly analyzed and summarized.The characteristics of the basic Kalman filtering algorithm are introduced,and the derivation process and other filtering algorithms are presented in detail.3.Based on the knowledge base of SINS/GPS integrated navigation system,the mathematical model is established,and the position and velocity information of SINS receiver is used to modify the position and speed of GPS receiver.On top of this,the system error is modeled by the BP neural network,so as to further improve the Kalman filter gain matrix coefficients of the modified Kalman filtering algorithm and theexperimental simulation.4.In view of the uncertainty of the parameters and noise statistics of the dynamic model in the nonlinear system,the combined navigation system is analyzed and simulated by AKF and UKF algorithm.