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Erschienen in:
2011 | OriginalPaper | Buchkapitel
A Novel Hybrid Grey-Time Series Filtering Model of RLG’s Drift Data
verfasst von : Guo Wei, Jin Xun, Yu Wang, Xingwu Long
Erschienen in: Computing and Intelligent Systems
Verlag: Springer Berlin Heidelberg
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In order to shutdown the random drift of mechanically dithered RLG’s output data effectively, a new method named Grey-Time series modeling is proposed, which has integrated the Metabolic GM(1, 1) model and Time series model. Kalman filter is used to filter the drift data based on the model which has been built, and the Allan variance is adopted to analyze the data of gyro before and after modeling and filtering. The results show that: the effect on inhibiting RLG’s random drift by using this new method is better than that of traditional time series modeling and succedent Kalman filter. The method effectively decreases random error in each term of RLG, in which the improvement on quantization error is quite obvious.