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首页> 外文会议>International Conference on Mechanical and Aeronautical Engineering >Research on Time-series Modeling and Filtering Methods for MEMS Gyroscope Random Drift Error
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Research on Time-series Modeling and Filtering Methods for MEMS Gyroscope Random Drift Error

机译：MEMS陀螺随机漂移误差的时间序列建模和过滤方法研究

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摘要

The precision of MEMS gyroscope is reduced by random drift error. This paper applied time series analysis to model random drift error of MEMS gyroscope. Based on the model established, Kalman filter was employed to compensate for the error. To overcome the disadvantages of conventional Kalman filter, Sage-Husa adaptive filtering algorithm was utilized to improve the accuracy of filtering results and the orthogonal property of innovation in the process of filtering was utilized to deal with outliers. The results showed that, compared with conventional Kalman filter, the modified filter can not only enhance filter accuracy, but also resist to outliers and this assured the stability of filtering thus improving the performance of gyroscopes.
机译：MEMS陀螺仪的精度降低了随机漂移误差。本文应用了时间序列分析，对MEMS陀螺模型随机漂移误差。基于建立的模型，采用卡尔曼滤波器来补偿误差。为了克服传统卡尔曼滤波器的缺点，利用Sage-Husa自适应滤波算法来提高滤波结果的准确性，利用滤波过程中的创新的正交性来处理异常值。结果表明，与传统的卡尔曼滤波器相比，改进的过滤器不仅可以提高过滤器精度，而且还可以抵抗异常值，并确保过滤的稳定性，从而提高陀螺仪的性能。

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