Abstract
As the environmental temperature variation is one of the most important factors influencing the zero-bias of Fiber Optic Gyro (FOG), the temperature drift characteristic of FOG zero-bias was analyzed and its model method was studied. A new approach based on Gaussian process regression (GPR) was introduced to deal with the complex nonlinearity of FOG zero-bias against temperature. Through some small training set, the nonlinear mapping relationship between FOG zero-bias and temperature could be established accurately. Compared with Least Square primary and quadratic regression methods, it is shown that the model built by the new approach is able to obtain higher accuracy and the fitting error is reduced by an order of magnitude. Meanwhile, the case study shows that the GPR model is feasible, easy to be implemented, and very attractive for a wide application.
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Zhikun, H., Guangbin, L., Xijing, Z., Jian, Y. (2013). Temperature Model for FOG Zero-Bias Using Gaussian Process Regression. In: Du, Z. (eds) Intelligence Computation and Evolutionary Computation. Advances in Intelligent Systems and Computing, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31656-2_6
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DOI: https://doi.org/10.1007/978-3-642-31656-2_6
Publisher Name: Springer, Berlin, Heidelberg
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