Nonlinear Filtering Algorithm And Its Application In Integrated Navigation

As an interdisciplinary subject,navigation and positioning technology plays a more and more important role in Earth Science,information science,smart city,Internet of things life and automatic driving.For more and more complex realistic scenes and positioning requirements,a single navigation system is difficult to meet these requirements due to its own limitations.Integrated navigation has become the focus of navigation and positioning research.Integrated navigation combines two or more navigation methods to make up for the shortcomings of a single navigation system,so as to improve the accuracy and performance of navigation and positioning.A great majority of the target state estimation problems in integrated navigation are nonlinear filtering problems.Most of the common nonlinear filtering algorithms have more or less shortcomings,such as the error created by model linearization.This paper focuses on the research of nonlinear filtering algorithm.The main research works are elaborated as follows:(1)Firstly,various commonly used nonlinear filtering algorithms are studied and analyzed,and their filtering performance is compared through computer simulation.Then the principle of firefly optimization algorithm is analyzed and the algorithm steps are described.On this basis,it is improved,mainly including relative firefly fluorescence brightness solution method,attraction solution method and firefly position update method,the dynamic adjustment step size factor and dynamic difference factor are introduced,and the improved firefly algorithm is used to optimize the particle filter,so that the particle swarm optimization can be concentrated to the high likelihood region as much as possible,so as to ensure the overall quality of the particle swarm optimization.Through computer simulation,the results show that the improved particle filter has more advantages in accuracy and real-time than the original firefly optimized particle filter algorithm.(2)Secondly,it focuses on the basic working principle of inertial navigation system,the updating algorithm of attitude matrix and the analysis of system error.Aiming at the problems of low computational efficiency of particle filter(PF)algorithm and the reduction of computational accuracy caused by particle dilution caused by resampling,the improved step size dynamic adjustment firefly algorithm is introduced into the process of PF,and a step size dynamic adjustment firefly swarm optimization particle filter algorithm is proposed.The optimized algorithm is applied to heading estimation,and the filtering performance is verified by experiments.The results show that the algorithm has more advantages in nonlinear systems.