【作者】 王超；

【导师】 吴刚；

【作者基本信息】 中国科学技术大学 ， 控制科学与工程， 2015， 硕士

【摘要】 光纤陀螺是基于Sagnac效应工作的角速率传感器,具有全固态、体积小、质量轻、动态范围宽、启动快、成本低、易集成等突出特点,在军事和民用领域备受世界各国关注,获得了广泛应用。然而,受元器件性能、寄生效应和周围环境的影响,光纤陀螺的输出易产生漂移,影响惯性导航系统的精度。因此,抑制光纤陀螺漂移误差,提高其测量精度具有重要意义。本文设计并实现了光纤陀螺试验平台,该平台为文中光纤陀螺试验提供支持；并基于Labview开发了光纤陀螺试验平台软件,有助于提升试验的自动化水平和测试效率。本文以数字闭环干涉型光纤陀螺为研究对象,具体说明了它的系统结构、工作原理及性能指标。本文利用Allan方差法实现光纤陀螺随机漂移特性的评价；分别使用时间序列分析法、卡尔曼滤波器和前向线性预测(Forward Linear Prediction, FLP)滤波器实现光纤陀螺随机漂移序列的建模与滤波。本文先预处理实测的光纤陀螺随机漂移序列,再对该序列建立自回归滑动平均模型(Auto Regression Moving Average, ARMA),进而得到状态空间模型,构建卡尔曼滤波器,实现序列的滤波,并利用Allan方差法识别、量化光纤陀螺随机漂移序列中的五种主要误差项,通过比较滤波前后序列的零偏稳定性和各误差项系数值来评价滤波效果。本文对FLP滤波器提出了改进的迭代步长参数选取方法,通过试验验证该方法使FLP滤波器在静态、动态光纤陀螺测试中均能得到较好的滤波效果。本文提出了一种改进AR模型和建模方法实现光纤陀螺温度漂移的建模、预测及补偿,并在此基础上开发了光纤陀螺温度特性评价软件。本文分析了光纤陀螺的Shupe效应,使用实测数据建立改进AR模型,设计试验验证模型有效性,并利用模型预测值补偿陀螺输出。基于Labview开发的光纤陀螺温度特性评价软件对快速评价、预测光纤陀螺温度特性以及提高光纤陀螺测量精度具有实用价值。更多还原

【Abstract】 Fiber optic gyroscope(FOG) is a kind of angular rate sensor based on Sagnac effect with solid state, small size, light weight, wide dynamic range, fast starting, low cost, high reliability and so on. Many countries pay much attention to FOG for the military and civilian purposes. FOG is widely used in the world because of its outstanding features. However, FOG is easily affected by the performance of its components, parasitic effects and the surroundings. This leads to the FOG output drift and deteriorates the precision of the inertial navigation system. Therefore, it is significantly important to suppress the FOG drift error to improve the FOG measurement accuracy.This thesis designs and implements a FOG testing platform which provides support for the FOG experiments. The corresponding FOG testing platform software is developed based on Labview, which is helpful to improve the level of automation and the efficiency of experiments. This thesis is based on the digital closed-loop interferometric FOG, illustrating its system structure, working principle and performance indexes.Allan variance method is utilized to evaluate the FOG random drift characteristic. Time series analysis, Kalman filter, and FLP filter are utilized to model and filter the FOG random drift sequence. This thesis firtly preprocesses the FOG random drift sequence and secondly builds an ARMA model. A state space model is induced through the ARMA model, and then a Kalman filter is built to filter the sequence. This thesis uses Allan variance method to identify and quantify five main error terms in the FOG random drift. Bias stability and five error term coefficients are used to assess the filtering effect. A better step size selection rule is proposed for the iterative procedure of the FLP filter. The experiments show that the FLP filter based on the proposed method gets a good filtering effect in the static and dynamic tests of the FOG.An improved AR model and its modeling method are proposed in order to model, predict and compensate the FOG temperature drift sequence. Having the model at hand, a FOG temperature characteristic evaluation software is developed. This thesis analyzes Shupe effect of the FOG, and collects the temperature drift data to build the improved AR model, and validates the effectiveness of the model through the temperature experiments. The FOG output can be compensated by the predictive value of the model. A FOG temperature characteristic evaluation software is developed with Labview, marking it valuable for engineering application in rapid assessment and prediction of FOG temperature characteristic and improvement of FOG measurement accuracy.更多还原