Research Of Positioning Technologies Combined Dual Mode Satellite With MEMS

Satellite positioning system has been widely used in vehicles,aerospace,ships and other fields,but the single-mode system can no longer meet the needs of high accuracy and high reliability in positioning accuracy and stability.In addition,the number of observable satellites in the single-mode satellite positioning system is reduced due to signal occlusion,which leads to the decrease of positioning accuracy and the failure of continuous positioning.Therefore,GPS(Global Positioning System)/MEMS(Micro-electro-mechanical System)and other combined Positioning technology gradually developed to solve these problems;It has become a research hotspot in the field of positioning technology.Based on the combination of BDS(Bei Dou Navigation Satellite System)/GPS dual-mode satellite and MEMS strapdown inertial navigation,this paper conducts research on positioning technology.Aiming at the problems of traditional Unscented Kalman Filter(UKF),such as filtering divergence and poor anti-interference ability,the stability,robustness and parameter value of the filtering system are improved to improve the positioning accuracy of the combined positioning system.Specific research contents include:Firstly,the research background and significance of Global Navigation Satellite System(GNSS),MEMS inertial navigation system,and the combined positioning technology of dualmode Satellite and MEMS strapdown inertial navigation system.The positioning principle and technology of the existing system are analyzed,and the problems existing in the positioning technology are summarized.Aiming at these problems,the solution is pointed out and the research content is determined.Secondly,because the UKF algorithm is easily interfered by external factors in the combined positioning system,there are certain errors in the obtained measurement information and noise covariance matrix,resulting in a decrease in filtering accuracy.To solve this problem,this paper proposes an improved UKF(IUKF)algorithm,which values the adaptive factor by judging the state of the system filter,and then revises the state prediction covariance matrix,measurement covariance matrix,update state covariance matrix,process noise covariance matrix and observation noise covariance matrix.To improve the anti-interference ability of system filtering,and overcome the problems of UKF filtering precision reduction and divergence caused by system inaccurate interference.Taking NRF51822 as the main control,ATGM336H-5N as BDS/GPS dual-mode satellite positioning module,MPU6050 as MEMS strapdown inertial navigation module,combined with MATLAB to design the semi-physical simulation platform of the system,from the aspects of simulation waveform,maximum and minimum error,mean error etc.The existing algorithms are compared to verify the effectiveness of the algorithm.Finally,in order to improve the filtering accuracy and stability of the system,an IUKF algorithm for lion swarm optimization is proposed.Loin Swarm Optimization(LSO)was introduced to optimize the covariance matrix of process noise and the initial value of observation noise matrix in the system filtering,which solves the problem that the filtering accuracy of the system decreases due to its improper initial value.It the meantime,it saves much time and energy for manual repeated experiments.The hardware-in-loop simulation experiment was carried out by combining hardware platform and MATLAB,and the reliability of the algorithm was verified by comparing the simulation waveform,maximum and minimum error,mean error and other aspects with the existing algorithm.Based on the combination of BDS/GPS/MEMS,this paper proposes an improved UKF and introduces the Lion Swarm Optimization algorithm to improve the positioning accuracy of the system.The results show that the proposed method can not only effectively improve the filtering accuracy,increase the system stability,but also improve the system robustness.It can effectively promote the development of dual-mode satellite/MEMS combined positioning technology and provide theoretical support for the wide application of the system.