Abstract
Bias of ring-laser-gyroscope (RLG) changes with temperature in a nonlinear
way. This is an important restraining factor for improving the accuracy of RLG.
Considering the limitations of least-squares regression and neural network, we
propose a new method of temperature compensation of RLG bias-building function
regression model using least-squares support vector machine (LS-SVM). Static and
dynamic temperature experiments of RLG bias are carried out to validate the
effectiveness of the proposed method. Moreover, the traditional least-squares
regression method is compared with the LS-SVM-based method. The results show the
maximum error of RLG bias drops by almost two orders of magnitude after static
temperature compensation, while bias stability of RLG improves by one order of
magnitude after dynamic temperature compensation. Thus, the proposed method reduces
the influence of temperature variation on the bias of the RLG effectively and
improves the accuracy of the gyro scope considerably.
© 2011 Chinese Optics Letters
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