一种新型的基于灰色模型的动调陀螺随机漂移建模方法

A Novel Grey-Based Modeling Strategy for a Dynamically Tuned Gyroscope Random Drift Model

为了减少动调陀螺仪(DTG)随机漂移的建模误差,提出了一种基于灰色理论的新型混合建模方法.此方法将小波分析理论引入到灰色模型中,旨在提高单变量一阶灰色模型GM(1,1)的建模能力.原始的DTG漂移数据首先经小波变换处理后,其冲击干扰噪声被抑制,然后用预处理后的漂移数据建立灰色模型,最后再施以小波逆变换.用实测的DTG漂移数据对此方法的有效性进行验证,结果表明该混合建模方法能够给出满意的建模特性.

In order to reduce the drift modelling error of gyroscopes, the drift characteristics of gyroscopes was analyzed and it was pointed out that drift error is one of the main factors that determine the performance of inertia navigation system. Thus it is significant to process the drift data of gyroscopes. Based on the analysis of the existing methods, this paper addressed a grey-based model for reducing the modelling error of drift data for a dynamically tuned gyroscope (DTG). Wavelet transform (WT) is integrated into the grey model to enhance modeling capability of the GM (1,1), which is a single variable first-order grey model. The raw DTG drift data are preprocessed by the WT to eliminate disturbing impactive noises. The post-processed data are then used to construct the grey model. The numerical results from measured drift data of a DTG demonstrate that the proposed hybrid strategy can reduce the drift model error and improve the model accuracy. And the modelling performance of the presented hybrid model is quite satisfactory.