Research Of SINS/GPS Tightly Integrated Algorithm Based On UKF

It is one of the main directions of the current navigation field to integrate the output parameters of Strap-down Inertial Navigation System(SINS)and Global Positioning System(GPS)for data fusion and get a better performance integrated system.Unscented Kalman filter(UKF)is a kind of data fusion algorithm in the Kalman filter framework.UKF replaces the traditional method of linearizing nonlinear function with Unscented Transformation(UT)in the framework of Kalman filtering,which makes the system subject to minimal linearization error and obtains high calculation accuracy.Therefore,UKF has been widely used in engineering since it was proposed.Secondly,in the SINS/GPS tight integration system studied in this paper,the pseudo range and pseudo range rate provided by GPS receiver are used in system correction,and the speed and accuracy of navigation constellation selection will directly affect the effect of correction.To sum up,this paper will carry out research from two main aspects: GPS satellite selection algorithm and SINS/GPS integrated system data fusion algorithm.The work contents are as follows:First of all,from the navigation reference coordinate system,common navigation parameters and significance,and the basic principle of strapdown inertial navigation,this paper deduces the solution equation of strapdown inertial navigation,and describes the basic principle and main error sources of GPS navigation system.Then the basic principle of nonlinear Kalman is introduced,and its advantages and disadvantages in different application modes of integrated navigation system are discussed in detail.Secondly,aiming at the GPS system,this paper focuses on the accuracy factor of GPS and analyzes the influence of satellite geometry distribution on positioning accuracy,discusses the performance of traditional satellite selection algorithm,and then simulates the number of GPS visible satellites based on ephemeris information.When the number of visible satellites is large,the problem of large amount of calculation of traditional star selection algorithm will aggravate.A new fast star selection strategy is proposed.Through the simulation of matlab ephemeris,combined with the optimal constellation combination and its geometric accuracy factor value,the effectiveness of the proposed algorithm is verified.Thirdly,this paper introduces UKF Algorithm in detail,and focuses on the influence of sigma points acquired by different sampling strategies on UKF algorithm.In order toimprove the real-time performance of the filtering algorithm on the premise of ensuring the filtering accuracy,the minimum skewness simplex sampling method is used instead of the traditional symmetrical sampling method,and the adaptive scaling factor is used to modify the real-time sampling process of UT.Finally,when the state equation of the system is linear,the simple form of KF algorithm is introduced in the process of time updating of UKF algorithm,which avoids the extra calculation caused by UT in the process of time updating of traditional UKF algorithm.Then through matlab simulation and comparison with Extended Kalman Filter(EKF)and traditional UKF algorithm,the performance of the improved adaptive scaled modified Unscented Kalman Filter(ASMUKF)in real-time and accuracy is verified.Finally,the SINS/GPS Tightly-Coupled Integration system model is established,including the measurement equation suitable for EKF algorithm and the nonlinear measurement equation suitable for UKF algorithm.Combined with the content of satellite selection algorithm,the EKF algorithm,the traditional UKF algorithm and the improved ASMUKF algorithm are used for matlab simulation.Through the application in the integrated navigation system,the excellent characteristics of the improved ASMUKF algorithm in the high-dimensional integrated navigation system and the effect of the improved satellite selection algorithm in improving the real-time performance of the system are verified.