Calibration And Offshore Alignment Of Marine Strapdown Inertial Navigation System Based On Fiber Optic Gyro

With the precision improvement of the fiber optic gyro (FOG), marine FOG strapdown inertial navigation system (SINS) is at the product development stage. The key technologies of marine FOG strapdown inertial navigation system include the calibration technology, initial alignment technology, comprehensive calibration technology, damping technology et al. According to practical requirements of marine FOG strapdown navigation system, this paper mainly focuses on the research of the calibration technology and offshore initial alignment technology, which can be summarized as follows:Firstly, by establishing the FOG unit error model and the accelerometer unit error model, the relationship between the calibration error, the body angular rate and the acceleration are analyzed. The equation between the calibration error and strapdown inertial navigation system error is derived. The navigation error brought by the calibration error under the swaying motion and uniformly accelerated rectilinear motion is derived and the analysis is confirmed by simulation results.Secondly, the error analysis of schism calibration method and simulations are performed, which shows the inertial measurement unit (IMU) calibration error brought by the velocity error and orientation error of three-axis turntable. A 6-position discrete calibration method is proposed for FOG unit, which reduces the FOG unit calibration error brought by the three-axis turntable error and eliminats the coupled error among FOG parameters, and finally improves the calibration precision. Simulation and navigation experiments confirm that 6-position discrete calibration method improves the calibration precision.Thirdly, FOG unit closed-loop calibration method and accelerometer unit closed-loop calibration are performed, in which the result of open-loop calibration is used as initial value of closed-loop calibration. The attitude provided by the three-axis turntable is used to revise the FOG scale factor and installation error, and the velocity provided by the three-axis turntable is used to revise the accelerometer scale factor and installation error. The calculation of the closed-loop calibration will continue until the difference of parameters between two neighboring revision is smaller than a given value. Experimental results show that the closed-loop calibration method can increase the system calibration precision.Fourthly, a 10-poistion systematic calibration is proposed by using the calibration error in the static velocity error equation and attitude error equation. In this method, Kalman filter is used to estimate the calibration error by utilizing the three-axis turntable's velocity and attitude. One property of Kalman filter is that the tracking speed is quick at the beginning. Based on this property of Kalman filter, a multistage systematic calibration method is designed in this paper. The calibration of multistage systematic calibration is the same as 10-poistion systematic calibration, but it reuses the Kalman filter to estimate the calibration error. Simulation and experiment results show that this multistage systematic calibration method can improve the calibration precision of FOG.Finally, an offshore initial alignment with large azimuth misalignment method is proposed in this paper, which is based on a mixed dynamic filter algorithm. is applied in the offshore initial alignment with large azimuth misalignment. The misalignment between the theory navigation coordinate frame and the calculated navigation coordinate frame are indicated by Euler platform error angles. Derive the strapdown inertial navigation system error equation under large azimuth misalignment and simplify it to meet the offshore fine initial alignment model of marine fiber optic gyro strapdown inertial navigation system. Describe the mixed dynamic filter model. It described the EKF-KF and UKF-KF in detail and gave the integration of linear estimate and non-linear estimate. Separate the non-linear state from the linear state. The nonlinear filters that are EKF and UKF are used to estimate the nonlinear state which is azimuth misalignment. The linear filters KF is used to estimate the linear state which are horizontal misalignment. The EKF-KF and UKF-KF take the estimate variance of azimuth misalignment as a feed bake to revise the horizontal misalignment estimate. It can improve the estimate precision. The simulation proved the validity of the mixed dynamic filter algorithm.