【摘要】 光纤陀螺(FOG)随机漂移严重影响捷联罗经系统初始对准的精度。通过对陀螺随机漂移进行建模,然后设计合适的卡尔曼滤波器进行滤波,可以减少随机漂移。在建模过程中,首先要对光纤陀螺随机漂移数据进行平稳性,正态性和零均值检验,之后进行数据预处理。通过相关函数的特点"拖尾"或者"截尾"来进行模型识别,采用最终输出误差准则选择模型的最佳阶数,然后采取Levison关系约束的Burg算法求解模型参数。根据模型建立系统方程和观测方程,然后进行卡尔曼滤波。实验结果表明,基于AR(3)模型的卡尔曼滤波可以有效的减少随机误差。这种方法不仅可以提高初始对准精度,也可以缩短对准时间。更多还原

【Abstract】 Fiber Optic Gyro(FOG) random errors seriously affect the initial alignment accuracy of strapdown FOG compass system. In order to minimize FOG random error, a method for time sequence modeling of FOG random drift was presented, and Kalman filter was built. In the process of modeling, statistical test and corresponding pretreatment of FOG drift was essential, as the estimation procedures were available only for stable, normal and zero-mean series. Model was selected by judging the characteristic of "tail off" and "cut off", and the order was determined by final prediction error(FPE) criterion. Burg algorithm based on Levinson constraints was adopted to solve the model parameters. Based on Auto Regressive Moving Average(ARMA) model, system equations and observations were established, and Kalman Filter was carried out. The experimental result shows that Kalman Filter, which bases on AR(3) model, can effectively eliminate random errors. Not only the method improves the alignment accuracy, but also shortened the alignment time.更多还原