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随机漂移是影响光纤陀螺(FOG)导航精度的重要因素,建立数学模型并进行补偿是一种减小其影响的简易而有效的方法。首先通过分析FOG输出序列的自相关函数,得出其时间相关性呈较弱现象,继而分别使用Yule-Walker算法及最小二乘法求出线性AR及非线性AR(NAR)模型参数,最后比较各模型的滤波效果以确定要采用的模型及其阶数,并进行模型适用性检验。验证结果表明,AR(1)与NAR(1)模型均适合FOG随机漂移的建模及实时滤波,且能较好改善其零偏稳定性。
Random drift is an important factor affecting FOG navigation accuracy. Establishing a mathematical model and compensating for it is a simple and effective method to reduce its influence. Firstly, by analyzing the autocorrelation function of the FOG output sequence, the temporal correlation is weaker. Then the parameters of linear AR and nonlinear AR (NAR) models are obtained by Yule-Walker algorithm and least square method respectively. Finally, The filtering effect of the model to determine the model to be used and its order, and test the applicability of the model. The verification results show that AR (1) and NAR (1) models are both suitable for FOG random drift modeling and real-time filtering, and can better improve the bias stability.