A Multivariate Temperature Drift Modeling and Compensation method for Large-diameter High Precision Fiber Optic Gyroscopes

Fiber optic gyroscope (FOG) plays a critical role in aerospace, marine transportation, geological exploration, and other fields because of its advantages of low cost and broad development prospects, etc. In recent years, there has been a variety of temperature drift compensation methods overcoming the temperature instability of FOGs. However, with the advent of large-diameter high precision FOGs (diameter greater than 200 mm), which are more susceptible to unstable ambient temperature, few previous studies have been conducted from the perspective of complex multivariate temperature field. In this paper, a temperature drift compensation method based on a multivariate temperature field is proposed to fill this gap. Combing the theoretical basis of FOG and the structure of a large-diameter high precision FOG, a multivariate temperature drift model is analyzed and established, and support vector regression (SVR) is utilized to train the temperature drift model. To improve the modeling capability, variational mode decomposition (VMD) is introduced to accurately extract the temperature drift signal and the model parameters are optimized by particle swarm optimization (PSO). The results of multi-channel variable temperature experiments verified the feasibility and superiority of this method, which is expected to lay the foundation for the application of this kind of FOG in inertial navigation systems.