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The performance of the Fiber Optical Gyroscope (FOG) is susceptible to the external environment temperature, and the temperature effect of the FOG has nonlinear characteristics. Therefore, it is necessary to develop an outstanding temperature compensation method with high precision and good versatility. In this paper, the severe influence of environmental temperature on the FOG's accuracy was analyzed in detailed. Then the temperature error model of the FOG was established. Based on the function fitting capability of the Support Vector Machine (S VM), a new modeling and compensation method of the gyroscope's temperature error was proposed. To verify the advantages of this novel method, we compared it wi th other two compensation methods, which are based on the classical statistical estimation parameters (polynomial regression) and the neural network, respectively. Experiment results showed that the proposed method can decrease the temperature error of the FOG by 75% and improve the adaptability of the environmental temperature. With this novel method, the gyro bias stability can meet the practical need of the temperature compensation which requires high- precision FOG.

Modeling and Compensation Method for Temperature Error of FOG based on the SVM