中文摘要：

建立光纤陀螺随机漂移模型以便在滤波中加以补偿是提高光纤陀螺输出精度的有效方法。针对传统光纤陀螺随机漂移建模均采用离线形式，需预先处理数据，不具备普适性等问题，提出一种实时的建模滤波方法。首先，根据大量实测数据对传统离线模型进行改进，研究了一种基于AR模型的在线建立光纤陀螺随机漂移模型的方法。然后，比较了传统Kalman滤波器与H∞滤波器用于实时滤波的效果。实验结果表明，改进型AR模型拟合精度高、普适性强，单个噪声拟合精度最低值为91.6%。H∞滤波器效果优于传统的 Kalman 滤波器，分析单个噪声滤波效果时，H∞滤波器较Kalman滤波器性能最多可提高38.5%。

英文摘要：

Establishing the model of FOG’s random drift and compensating in the filter is an effective method to improve the output precision of FOG. For traditional random drift of fiber optical gyroscope has some shortages like off-line, needing pre-process and the off-line models, which are usually not universal for environmental changing, a new modeling and filtering way is put forward. First, based on a large amount of measured data, the traditional off-line AR model is improved, and a new method to build the model of FOG’s random drift is studied. Then, the comparison is made between the traditional Kalman filter andH∞ filter in real time. The result demonstrates that improved AR model has much applicability and the performance ofH∞ filter is better than Kalman filter. The minimum value of fitting accuracy is 91.6% andH∞ filter can improve the performance of filtering by almost 38.5% when analyzing ingle noise.