Methods of improving the accuracy of Fiber Optic Gyro test

This paper studies how to improve the accuracy of the Fiber Optic Gyroscope (FOG) test by editing proper test procedure and designing new data processing means. A testing means of two reverse orientations is presented in the test of zero bias and input axis misalignment, and this means is effective to avoid the errors brought by the positioning fixture. In the scale factor test, in order to reduce the error brought by instability of the gyroscope output, a reasonable way of testing the static output is designed. And the paper designs an effective data processing method to improve the testing accuracy. At first, Pre-processing is needed after receiving the datum, which can remove the occasional error. And then develop a random error model of FOG based on the analysis of time series. In modeling, a long Auto-Regression (AR) model is used to estimate the parameters of the Auto-Regression-Moving-Average (ARMA) model and this method avoids solving nonlinear equations and has the convergence property. Through comparing all kind of models, it is found that low-order AR or low-order ARMA model can well fit the output series. Then, a Kallman filter based on difference is presented Through the experimental testing, it is effective in reducing the random error, and the results show that the random error is reduced to about the original 50.3% through the filter.