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

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.