对光纤陀螺随机噪声的自回归建模和辨识方法进行了分析和比较,并对自回归模型下卡尔曼滤波降噪算法进行了改进。针对传统方法系统噪声和量测噪声必有一个为有色噪声的问题,提出了新的有色噪声条件下的卡尔曼滤波处理方法:把有色量测噪声作为状态量扩充到系统的状态方程,同时把AR模型的建模误差考虑进来作为系统新的量测噪声。实验结果表明,提出的滤波方法能够有效的抑制陀螺随机漂移,提高姿态解算精度。

Modeling and filtering methods of fiber optic gyroscope(FOG) random noise based on auto-regressive (AR) model were analyzed and contrasted, and the Kalman filtering algorithm based on AR model was improved. A new method of whitening colored noise for Kalman filter was introduced to solve the problem of traditional methods in which there must be a colored noise between system noise and observation noise. Test results demonstrate that the proposed filtering method can effectively reduce FOG random noise and improve accuracy of the attitude determination.