基于均值逼近算法的光纤陀螺温度补偿方法  

Compensation Method for Temperature Error of Fiber Optical Gyroscope Based on Mean Approximation Algorithm

作　　者：石雪 胡宗福[1] 傅长松 

机构地区：[1]同济大学电子与信息工程学院,上海201804 [2]上海航天控制技术研究所惯导部,上海201109

出　　处：《佳木斯大学学报（自然科学版）》2018年第1期37-41,共5页Journal of Jiamusi University：Natural Science Edition

基　　金：上海航天科技创新基金资助(SAST2015076)

摘　　要：在机器学习温度补偿算法的基础上,一种基于高斯分布的均值逼近算法,能更好的减小光纤陀螺零漂的温度依赖性。实验与仿真结果表明,在环境温度从-40℃到60℃变化的情况下,通过均值逼近方法,补偿后的光纤陀螺的零偏输出减小了95%,光纤陀螺的零偏精度可以达到0.05°/h。因此,在环境温度变化的情况下,与其他温度补偿方法相比,均值逼近算法可以更有效地提高光纤陀螺的适应性。According to the theory of machine learning algorithms, the mean approximation algorithm, based on Gaussian distribution, can reduce the temperature dependency of the fiber - optic gyroscope. On the basis of the result of the experiment, the output of fiber- optic gyroscope is reduced by 95%, the precision can come up to 0.005 （°）/h when the environment temperature changes. Therefore, compared to other algorithms, the mean approximation algorithm is the more effective method to enhance the temperature adaptability of the fiber - optic gyroscope.

关 键 词：光纤陀螺 温度 机器学习 均值逼近 

分 类 号：V241.5[航空宇航科学与技术—飞行器设计]