Compensation method of FOG temperature drift with improved support vector machine

Temperature drift is one of the main factors that affect the accuracy of fiber optic gyroscope (FOG), and its modeling and compensation are effective methods to eliminate and reduce the drift. The key factors that affect the temperature drift of FOG were analyzed. Meanwhile, the test experiment of FOG temperature drift was carried out. Then, the support vector machine which had better generalization ability than the neural network was used to regress and model the temperature drift of FOG, and the radial basis kernel function was adopted as the kernel function of support vector machine which had better data set adaptability. In order to improve the modeling accuracy of support vector machine, the artificial fish swarm algorithm was used to optimize the penalty factor C of support vector machine and the factor of kernel function. Finally, the proposed compensation method was verified by the actual temperature drift data of FOG, which showed that the remaining error of FOG compensated by the proposed method was reduced by 4-5 orders of magnitude than that compensated by the linear regression method.