Research on Signal De-noising Technique for MEMS Gyro

摘要：

To effectively wipe out random drift and extract valid signal of MEMS gyro, the methods of adaptive Kalman filtering and wavelet analysis are investigated. For the first method, the autoregressive moving average (ARMA) model of random drift is established, which is essential to the adaptive Kalman filter. For the second one, the wavelet basis, decomposition level, and threshold-choosing principle are determined. Then the de-noising test is implemented by using real signal of MEMS gyro, and both methods are of good effectiveness. The contrast analysis between both methods indicates that the adaptive Kalman filtering approach is more suitable for the real-time de-noising of MEMS gyro signal.

关键词： MEMS gyro:de-noising:adaptive Kalman filter:wavelet analysis

作者: Gannan Yuan Haibo Liang Kunpeng He Yanjun Xie

作者单位: College of Automation,Harbin Engineering University,Harbin 150001,China

会议类型: 国际会议

会议名称: The 3rd International Symposium on Systems and Control in Aeronautics and Astronautics(第三届航空航天系统与控制国际会议 ISSCAA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1281-1285

在线出版日期: 2010-01-08（万方平台首次上网日期，不代表论文的发表时间）