【摘要】 高精度光电稳定跟踪平台基准轴抖动或者缓慢漂移通常使得光纤陀螺(FOG)的输出信号中含有随机噪声。针对这一特点,通过对工程中实际采用的光纤陀螺实测数据进行时间序列分析,运用递推最小二乘法建立了噪声模型,并对其进行自适应卡尔曼(Kalman)滤波处理。通过Allan方差法分析结果表明,使用只对观测噪声协方差R阵进行自适应的Kalman算法滤波效果明显优于普通Kalman算法,且加入的计算量小,实时性能优于Saga-Huga自适应Kalman算法,对提高光电稳定跟踪平台性能有一定的实用价值。更多还原

【Abstract】 The datum axis of high precision photoelectric steady tracking platform jittering and drifting slowly often makes the output signal of fiber optic gyroscope(FOG) contain random noise. Based on the characteristics mentioned above, time series analysis of the data actually measured from FOG which is actually applied in engineering is conducted. Noise model is established by using the recursive least squares method, and it is processed with adaptive Kalman filter. With an exhaustive analysis by using the Allan variance method, it is shown that the filtering effect of Kalman algorithm that simply adapts the observation noise covariance matrix R is much better than that of normal Kalman algorithm, and the real-time performance is better than that of the Saga-Huga adaptive Kalman algorithm. For the Kalman algorithm, the amount of calculation added is small. This work has some practical value to improve the performance of photoelectric stable tracking platform.更多还原