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The temperature drift causes the zero-bias drift of the fiber optic gyroscope to show complex nonlinear changes, which seriously restricts the measurement accuracy of the fiber optic gyroscope. Therefore, it is necessary to establish an accurate temperature compensation model to compensate for the temperature drift of the fiber optic gyroscope.In order to effectively improve the output accuracy of the fiber optic gyroscope under the condition of the full temperature range, the static full temperature bias test of the fiber optic gyroscope is first designed to obtain the bias data of the fiber optic gyroscope under the temperature change condition of -40℃~60℃. Secondly, on the one hand, a polynomial regression model is gradually established with temperature, temperature change and multiple powers as independent variables. On the other hand, the RBF neural network model is established after screening the input variables with the MIV algorithm. Finally, two models are used to achieve zero-bias temperature compensation. According to the compensation results, both can effectively improve the full temperature output accuracy of the fiber optic gyroscope. Compared with the polynomial regression model, the RBF neural network model can identify temperature drift more effectively and accurately, and greatly improve the output accuracy of the fiber optic gyroscope in the full temperature range.
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