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As a mature product with high commercialization, the fiber optic gyroscope is susceptible to the influence of environmental factors in actual use, which affects the measurement accuracy. In order to improve the temperature adaptability of the fiber optic gyroscope and improve the efficiency of temperature compensation in the engineering process of the gyroscope, a temperature modeling and compensation method based on a multilayer perceptron is proposed. First, based on the working principle of the fiber optic gyroscope, the mechanism that causes the temperature error of the fiber optic gyroscope is analyzed. Then, based on the neural network model of the multilayer perceptron, the structure design of the temperature compensation model of the fiber optic gyroscope is carried out, and the existing data is used to train the model. Finally, the compensation model was verified by experiments. The results show that the bias stability of the gyro can be improved by 80% after compensation using this model. Although this method requires a lot of calculations in the early stage, after the model parameters are solidified, it has strong adaptability, is easy to implement in engineering, and can effectively improve engineering efficiency.
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