一种光纤陀螺随机漂移的非平稳时序建模法  

A non-stationary time series modeling method for random drift of fiber-optic gyro

作　　者：张庆 谈振藩[1] 柳贵福[2] 许德新[1] 梁莹[1] 

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001 [2]中国船舶工业集团公司船舶系统工程部,北京100036

出　　处：《传感器与微系统》2010年第7期43-46,共4页Transducer and Microsystem Technologies

基　　金：2008国际科技合作计划资助项目(2008DFR20420)

摘　　要：为减小光纤陀螺的随机漂移,针对其弱非平稳的特点,采用时间序列分析法建立其模型。提出一种全局最优的模型阶次搜索算法,把二维搜索化为一维搜索,得到了模型阶次的一致性估计。提出了一种改进的参数估计算法,它将非线性参数估计过程转化为线性过程,使用正置与逆置漂移时序共同估计参数,以前向和后向模型的滤波误差平方和最小为参数估计的指标,在(p+1)维空间中求极小值。对实测漂移进行了趋势项、周期项、自相关和偏自相关等分析,采用3种方式分别建立其非平稳模型,验证了上述算法的有效性。建模结果同时表明:直接辨识漂移模型的方式优于工程中常用的将非平稳漂移中的确定性模型和平稳随机模型分别辨识的方式。To reduce random drift of the fiber optic gyro, a model was set up using time series analysis method, aimed at weak non-stationary characteristics of random drift. A global optimum algorithm for selecting model order was put forward. It changed two-dimensional search program into one-dimensional one. Consistent estimation of model order was achieved. An improved parameter estimation algorithm was put forward. It transformed the nonlinear parameter estimation process into a linear one. Both measured sequence and reverse one were used to estimate parameters, so data information was fully utilized and the accuracy of parameter estimation was improved. The goal of parameter estimation is to get the minimum sum of forward and backward filtering error squares that was found in （p ＋ 1 ）-dimensional space. The trend terms, periodic terms, autocorrelation and partial autocorrelation of measured drift were analyzed. The statistical results of non-stationary models established from three ways verified the validity of above algorithms. And the results show that the way to identify drift model directly works better than the way to identify random model and non-stationary drift model respectively commonly used in engineering.

关 键 词：光纤陀螺 随机漂移 非平稳模型 ARMA模型 参数估计 

分 类 号：U666[交通运输工程—船舶及航道工程]