A Novel Method Based on a High-Dynamic Hybrid Forecasting Model for Fiber Optic Gyroscope Drift

摘要

The FOG is gaining increasing popularity because of its excellent performance. Reducing the drift of a FOG is the key to the performance of the entire INS system. To reduce FOG drift, we studied the use of the ARGM (1,1) and IARMA models to process the systematic and random drifts, respectively. We eventually achieved reconstruction using EMD by combining the strength of the two models. In addition, we used the Allan variance to estimate the drift data for a FOG. Although processing the drift of FOGs in this way does not use the latest techniques, we have improved the accuracy of the algorithm significantly. Numerical results demonstrate that ARGM (1,1) could overcome the drawbacks of using RGM (1,1) and also markedly increase the FOG accuracy and adaptability. Future work will focus on the effect of the proposed model when applied in different environments.