Research And Implementation Of BDS/MEMS-SINS Tightly Integrated Sustainable Navigation Algorithm

Miniaturization and integration is one of the development trends of navigation system.Satellite/inertial tightly integrated navigation has the advantages of high precision,strong anti-interference and moderate complexity.However,in practical application,due to bad weather,complex terrain and other effects,the system performance will decline and the navigation can not be sustained.The goal of this paper is to study the Bei Dou satellite navigation system(BDS)/MEMS-SINS tightly integrated sustainable navigation algorithm and its embedded implementation method.Firstly,the attitude angle calculation principle based on accelerometer and magnetometer,the basic principle of strapdown inertial navigation system and the mathematical model of BDS/MEMS-SINS tightly integrated navigation system based on federated filter are studied.Secondly,the Orthogonal Transform Cubature Kalman filter(TCKF)with less computation and better filtering performance than Cubature Kalman filter(CKF)is selected as the basic algorithm.And a robust measurement noise adaptive square root TCKF(RA-SRTCKF)algorithm is proposed.In this algorithm,the high maneuver criterion is used to judge the motion state of the carrier.When it is in the high maneuver state,the robust adaptive square root TCKF(H-SRTCKF)algorithm is used for filtering,otherwise the measurement noise adaptive square root TCKF(A-SRTCKF)algorithm is used for filtering.Simulation experiments show that the RA-SRTCKF algorithm has higher filtering accuracy and adaptability than H-SRTCKF,A-SRTCKF and SRTCKF.Thirdly,the BDS/MEMS-SINS tightly integrated sustainable navigation method based on support vector regression(SVR)and federated RA-SRTCKF is designed.According to the number of visible stars,this method divides the measurement information provided by the navigation receiver into credible and uncredible situations.When the measurement information is credible,the error estimated by federaled RA-SRTCKF is used to correct the navigation information calculated by SINS,and the training data of speed error SVR prediction model is saved at the same time.When the measurement information is not credible,the pseudorange sub-filter,pseudorange rate sub-filter and main filter are isolated,and the speed error SVR prediction model is trained by the training data saved when the measurement information is credible.The predicted speed error and the attitude angle error estimated by the attitude sub-filter are used to correct the navigation information of SINS.The simulation results show that the sustainable navigation method can ensure the system to continuously output high precision navigation information when the measurement information provided by the satellite navigation receiver is not credible.Fourthly,a real time pseudorange error compensation method based on differential and adaptive filtering for two-stage Beidou navigation receiver is proposed.It and the BDS/MEMS-SINS tightly integrated sustainable navigation method based on SVR and federated RA-SRTCKF are implemented on the hardware system of BDS/MEMS-SINS tightly integrated navigation based on dual core microprocessor OMAP-L138.The results of static and sports car experiments show that when the number of visible stars of Beidou is insufficient,the system can still output continuous and effective navigation information.The root mean square error of attitude angle is less than 2.7°,the root mean square error of speed is less than 0.6m/s,and the root mean square error of position is less than 4.6m.The research results of this paper provide a reference idea for the sustainable navigation performance improvement of the tightly integrated navigation system,and at the same time help to promote the development of Beidou navigation technology.