Design Of Integrated Navigation Filter Algorithm And Research On Its Performance Evaluation Method

Combining two or more navigation systems into a navigation system through a suitable method is called a combined navigation system.It can overcome the shortcomings of a single system and also improve the accuracy of the navigation system.Integrated navigation system has great military,civil value and broad application prospects,And filtering algorithm is a key technology in the integrated navigation system.whose performance has a great influence on the integrated navigation system.Conventional Kalman filter algorithm is a common filtering algorithm in integrated navigation system.However,in engineering practice,it is found that the accuracy of the conventional Kalman filter algorithm declines or even divergence occurs due to the difficulty in accurately estimating the system model and the noise model.Therefore,this paper introduces the adaptive Kalman filter algorithm to overcome the problems of the conventional Kalman filter algorithm in engineering practice.For different filtering algorithms,whether the performance is good or bad and whether it is suitable for the current navigation system needs to be determined through performance evaluation.In this paper,the performance evaluation of filter algorithm in integrated navigation system is studied in depth and designed.The main points of this paper are explained as follows:(1)Deeply analyze the most basic and core algorithms in integrated navigation systems,that is strapdown inertial navigation system attitude algorithm and conventional Kalman filter algorithm.The stance updating algorithm of strapdown inertial navigation system is programmed.The integrated navigation system is modeled and the general Kalman filter algorithm of integrated navigation system is designed.While the system motion model is modeled,and the simulation experiment is carried out.(2)The Sage-Husa adaptive Kalman filter algorithm is introduced to solve the problem that the conventional Kalman filter algorithm has,which is the problem of navigation accuracy due to the inaccuracy of the model and the calculation error caused by filter divergence.The reasons and countermeasures of filtering divergence are analyzed,based on which that some improvements of the Sage-Husa adaptive Kalman filtering algorithm are proposed.The SageHusa adaptive Kalman filtering algorithm based on the introduced UD decomposition is designed,simulated and also compared.(3)In the performance evaluation of existing filtering algorithms,the performance is evaluated by the difference between the actual values and the navigation parameters that obtained by strapdown algorithm and filtering algorithm.In practice,the accurate real values are often difficult to obtain.This paper studies a new performance evaluation algorithm based on analog measurement to overcome the dependence of the existing evaluation algorithm on the real value.Simulation experiments show that the new performance performance evaluation algorithm not only overcomes the dependence on the real value,but also achieves the same evaluation result as the existing evaluation algorithm.