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为了减小光纤陀螺(FOG)的随机噪声,利用时间序列分析法对FOG的随机噪声进行了分析与建模,并在建立的自回归滑动平均(ARMA(2,1))模型基础上,采用一种将改进递推增广最小二乘(RELS)算法和Sage-Husa自适应卡尔曼滤波算法相结合的方法,对采集的FOG静态输出随机噪声进行实时补偿,同时与标准kalman滤波算法进行仿真对比。仿真结果表明,该方法具有更好的补偿效果,可更有效地抑制FOG随机噪声。
In order to reduce random noise of fiber optic gyroscope (FOG), the random noise of FOG was analyzed and modeled by time series analysis. Based on the ARMA (2,1) model established, A method combining improved Recursive Augmented Least Squares (RELS) algorithm and Sage-Husa adaptive Kalman filter algorithm is proposed to compensate the static output random noise of FOG in real time and to simulate with the standard kalman filtering algorithm Compared. The simulation results show that this method has a better compensation effect and can suppress FOG random noise more effectively.