In this paper, we present a prediction and compensation method for Micro-Electro-Mechanical System (MEMS) gyroscope random drift, which is based on relevance vector machine. The relevance vector machine (RVM) model is established based on the feature of MEMS gyroscope random drift and the parameters are trained by the Expectation Maximization (EM) algorithm. By phase space reconstruction, the time sequence of random drift is accessed in the model. The final experimental results indicate that our proposed methodology can achieve both the least complexity of structure and goodness of fit to data, and also can predict the gyroscope random drift accurately. Furthermore, by compensating random drift using the predicting result, the precision of gyroscopes application could be improved well.
A signal processing technique for compensating random drift of MEMS gyros
01.08.2014
123035 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Multiscale fyzzy-adaptive Kalman filtering methods for MEMS gyros random drift
British Library Online Contents | 2009
|MEMS Gyros in Pedestrian Navigation
British Library Conference Proceedings | 2011
|Online Contents | 1994