Modeling Method for Random Errors of IFOG Based on Multivariate Variance Information

Accurate modeling of the random error of IFOG is an important prerequisite to suppress the random error from the algorithm level. At present, the mainstream IFOG random error suppression based on ARMA model has the problem that the order of the model cannot be accurately determined. To solve this problem, a recursive Allan variance calculation is firstly proposed. Then, based on the Allan variance information in each averaging time corresponding to the angular random walk error, the angular random walk variance at the sampling time is accurately estimated. Finally, taking the angular random walk variance information as a priori information, an improved modeling method is proposed to determine the optimal order of the ARMA model, and the accurate modeling of IFOG random error is realized. Simulation and experimental results show that the modeling method proposed in this article can more accurately reflect the random error information of fiber optic gyroscopes, and the Kalman filter based on this modeling method can effectively filter out angular random walk noise.