Sins Nonlinear Adaptive Kalman Filter-based Alignment Technology

The alignment accuracy and the quick reaction ability of SINS (Strap-down Inertia Navigation System) have deeply depended on the initial alignment, which is the key of SINS. When heading errors are considerably large, and external disturbance factors including wind, etc. are strong, the alignment accuracy drops distinctly .The linear alignment equation based on the small heading error cannot describe the system error characteristic exactly, and the standard Kalman filtering algorithm cannot satisfy the request of alignment accuracy. Therefore, it is essential to study the problem of the nonlinear alignment of SINS under the large heading errors.By deriving the initial alignment errors equation of SINS, and considering the gyro drift and the accelerometer bias, the paper first introduces the nonlinear errors model with large heading uncertainty. Then, through computer simulating, the paper discusses the reason of nonlinear robust extend Kalman filter divergence, and based on the practical errors model, simplifies the arithmetic of nonlinear robust extended Kalman filter. And then, because nonlinear robust expanded Kalman filter has high adaptive ability but diverges easily, the paper presents a modified nonlinear adaptive filter algorithm based on literature [ 1 ] , which not only could ensure the accuracy of alignment, but also could improve the ability of adaptive. Finally, when the extended Kalman filter, nonlinear strong tracking adaptive Kalman filter, nonlinear robust extend Kalman filter and modified nonlinear adaptive Kalman filter are used to simulate the nonlinear system, the paper analyses the level alignment accuracy and heading alignment accuracy of these filters under different errors, including model error and noise error. The computer simulation results show that the modified method can restrain the divergence effectively, improve the performance and accuracy of the initial alignment, has better adaptive ability.