A method for temperature error compensation in fiber-optic gyroscope based on machine learning

Abstract

This work presents a machine-learning based method for temperature error compensation of fiber optic gyroscope (FOG). Alternative to existing methods for constructing samples by single data point, a novel method of constructing samples by sequence is proposed, which fully considers the hysteresis and delay of temperature error. Moreover, temperature experiments with three distinct temperature trends, i.e., heating, holding and cooling, are conducted. Following the construction of training and test sets, the relevant training and test processes are completed using SVR algorithm. According to the degree of approximation in predicted value relative to the real value and the degree of improvement in gyro stability, the effects of various sample construction methods on the corresponding error compensation are analyzed. Considering the root mean square error (RMSE), mean absolute value error (MAE) and improvement-factor of FOG zero-bias stability as measurement indicators, this work proposes to construct samples by sequence with temperature trend feature-extraction, which effectively improves the overall accuracy of gyroscope. Allan variance is also utilized to verify the effectiveness of our proposed method. Lastly, this study also provides a theoretical guidance and reference for temperature error compensation in other types of sensors.