Fast initial alignment of GPS-assisted SINS system on moving base

Abstract:

To realize the estimation of gyro error by optimization-based in-motion alignment algorithm (OBA) and apply it to low precision SINS systems, a new fast in-motion alignment (FIMA) algorithm was proposed by combining the adaptive unscented Kalman filter algorithm with the OBA algorithm for SINS systems assisted by GPS. In the proposed algorithm, the relationship between the gyro constant drift and the misalignment angle was used to build the state equation, and the measurement equation was constructed by integrating the gravity acceleration and the ground speed. Since the system was nonlinear, the UKF algorithm was applied to estimate the misalignment angle and the gyro constant drift. Due to the uncertainty of measurement noise, an adaptive filtering algorithm was introduced to estimate the noise in real time. Results show that for low precision SINS systems, the proposed algorithm could converge the heading angle error to less than 3 degrees in about 15 s, and within 3 min, the heading angle error could be converged to less than 1 degree. Compared with the traditional nonlinear moving base alignment algorithm and the OBA algorithm, the proposed algorithm could realize rapid alignment under any misalignment angle. In addition, it could estimate the gyro constant drift online and compensate the misalignment angle of the system, which improved the alignment accuracy and convergence performance.