光纤陀螺随机漂移补偿中的数据融合方法（英文）

为提高光电平台的控制性能和稳定性,以平台反馈回路所用的光纤陀螺传感器为研究对象,对光纤陀螺角速率的历史输出、当前量测以及随机漂移进行融合补偿。采用双自回归模型确定了光纤陀螺时间序列输出的自回归多项式和光纤陀螺随机漂移的自回归关系。以陀螺当前输出为量测量,结合卡尔曼滤波算法将陀螺历史输出和历史随机漂移融合进状态方程,并进行随机漂移在线估计补偿。实验结果表明,光纤陀螺随机漂移的AR模型能达到90%拟合效果,经卡尔曼滤波补偿后随机漂移能降到1/10。该方法能很好地抑制光电平台三个框架轴光纤陀螺的随机漂移,补偿率为80%~90%。

Data fusion method in random drift compensation of fiber optic gyroscope

In order to improve the control performance and stability of photoelectric platforms, the fiber optic gyroscope sensor module in the feedback loop is investigated as a case study. The historical output of angular rate, current measurement and random drift of fiber optic gyroscope are fused and compensated. The autoregressive relationship between the FOG’s sequentially outputted autoregressive polynomial of and its random drift are determined with a double-autoregressive model. Then the current output of FOG is used as a measurement, while the historical output and the historical random drift of the FOG are integrated into the state equation of Kalman filter in order to estimate and compensate the FOG’s drift online. Experimental results show that the AR model of the FOG’s random drift can achieve 90% of fitting effect, the random drift can be reduced to 10% after the Kalman filter compensation. In addition, the proposed method can inhibit the FOG’s random drift on the three-axis stabilized platform, and the compensation rate reaches 80%~90%.