Research On Some Key Technologies For Single-axis Rotary Inertial Navigation System Based On Fiber Optic Gyroscope

The objective of this dissertation is to do the research and development on high precision single-axis rotary strapdown inertial navigation system (SINS) based on fiber optic gyroscope(FOG),and key techniques of single-axis rotary SINS,such as the auto-compensation techniques of single-axis rotary SINS, random error modeling and filtering method for high precision fiber optic gyroscope,initial alignment method, systematic calibration, etc.,are studied in theoretical and engineering aspects in this dissertation. A high precision single-axis rotary SINS based on FOG is developed, and a series of navigation experiments are carried out. The major contents of this dissertation are as follows:1. The error modulation mechanism of single-axis rotary SINS is studied in this dissertation.For the SINS, the gyro constant drifts will cause the position error accumulated over time, while the propagation laws of the gyros scale factor error and installation error can also be changed in the rotary SINS. The modulation mechanism of gyro constant drift, gyro scale factor and installation errors in single-axis rotary SINS are analyzed. The influence of angle measurement accuracy of the indexing mechanism is studied, and it is pointed out that the angular accuracy of the indexing mechanism is linear to the error of attitude angle output of the SINS. The navigation errors of SINS are analyzed in mathematical simulation environment for two motion states of Inertial Measurement Unit (IMU), i.e. the stationary motion and rotary motion.2. Random error modeling and filtering methods of FOG are analyzed in this dissertation.FOG is the core component of the single-axis rotary SINS, and the random error of the FOG is the most difficult error to compensate among all errors. In order to suppress the influence of random error on the performance of the navigation system, FOG random error model is established by using time series analysis method, and a decoupling adaptive Kalman filtering method for noise reduction is proposed. The method is validated by using the data from real system, and the experimental results show that the proposed method has higher estimation accuracy compared with the standard Kalman filtering method.3. Initial alignment method of single-axis rotary SINS is investigated in this dissertation. The observability of the navigation parameters can be improved by rotating the IMU in the process of fine alignment. The observability analysis of the navigation parameters is carried out by using the singular value decomposition (SVD) method. The analysis results show that the unobservable states of the system can be made observable by the periodic rotation of the IMU, and the observability of some states can also be improved. The rotation of the IMU will cause saw tooth velocity error, and the saw tooth velocity error can further cause the Kalman filtering output error. In order to solve this problem, the standard Kalman filtering is improved in this dissertation. The improved Kalman filtering is fully verified on the experimental prototype, and the experimental results show that the proposed method can effectively avoid the filtering output error caused by the rotation of the IMU.4. Accurate identification method of azimuth gyro drift is analyzed in this dissertation.Azimuth gyro drift can not be modulated in the single-axis rotary SINS, and it will cause the accumulated position error over time. In order to improve the precision of long time navigation for single-axis rotary SINS, an accurate method to calibrate the azimuth gyro drift is proposed. The horizontal damping network is introduced into the navigation algorithm to suppress the Shuler oscillation error. A mathematical model is established to connect the error of latitude and longitude with the axial gyro drifts and the angle error of initial heading, and in addition a reasonable calibration flow is designed, in which the least square method is used to identify the azimuth gyro drift accurately. Mathematical simulations and actual system verification experiments are carried out, and the results show that the proposed method can accurately identify the azimuth gyro constant drift of SINS, which can further improve the positioning accuracy of the inertial navigation system.5. A systematic calibration method of single-axis rotary SINS is analyzed in this dissertation.In order to improve the precision of long time navigation for single-axis rotary SINS, an on-line systematic calibration method for IMU error is presented. Error parameters of SINS are analyzed,and it is pointed out that the eastward drift and azimuth misalignment are the main error sources that affect the estimation accuracy of the azimuth gyro drift. Kalman filtering is used to estimate the misalignment angles of the SINS, and these estimated errors are compensated to the outputs of the system. After the compensation, the Kalman filtering is used again to estimate IMU error online,and the position matching Kalman filtering algorithm is presented. Mathematical simulations and actual system verification experiments are carried out, and the results show that the method proposed in this dissertation can accurately estimate the IMU error, and the positioning accuracy of the inertial navigation system is greatly improved after IMU error compensation.6. A high precision experiment prototype of single-axis rotary SINS is developed, and the research works include the design of the framework, the construction of software and hardware, the composition and implementation of navigation computer, etc.. Comprehensive experiments are carried out on the prototype of single-axis rotary SINS, mainly including IMU static experiments,single-axis rotation experiments under static or swing base, vehicle experiments, Fu-xian lake ship experiments and so on. The maximum positioning error of the navigation system is better than 0.3n mile/h, which meets the inertial navigation system design requirements.