动态环境下光纤陀螺误差辨识与补偿技术

Identification and compensation of FOG error under dynamic environment

为了提高光纤陀螺在高动态环境下的测量精度,需要精确地辨识角加速度信息以便有效地补偿。针对直接对陀螺的角速度信息微分处理后得到角加速度的方法误差较大的问题,提出了将微分后的角加速度信息分为线性和非线性两个部分,其中线性部分采用Savitzky-golay最小二乘拟合,而非线性部分则采用RBF神经网络技术进行拟合。上述处理方法能更真实地反映实际物理过程,具有较强的自适应性和较好的拟合效果。通过试验验证,证明了该方法的有效性和准确性,提高了角加速度辨识精度,比直接微分的方法测量精度提高二个数量级,有效地补偿了陀螺仪在高动态环境下的测量精度。

To improve the precision of fiber optic gyroscope（FOG） in high dynamic environment, the angular acceleration must be accurately identified to make effective compensation.In view that the FOG angular acceleration obtained by direct differentially processing the FOG angle velocity information has relatively large errors, the angular acceleration information is divided into the linear part and the nonlinear part.The linear part uses savitzky-golay algorithm to carry out least square fitting, while the nonlinear part uses RBF neural network（RBF NN） method to carry out fitting.This method has more strong adaptability and has better fitting effect because it can more truly reflect the real physical process.Finally, simulations are made to test and compensate the measurement errors of FOG angular velocity under high dynamic environment, which verifies that the algorithm is correct and effective, and the identification accuracy of the angular acceleration is improved by two orders of magnitude than that of the direct differential method, showing that the proposed method can effectively compensate the FOG measurement errors under high dynamic environment.