An adaptive UKF algorithm for silicon micro-machined Gyroscopes

被引：0

作者：

Ji X. ^{[1
,2
]}

Wang S. ^{[3
]}

机构：

[1] School of Automation, Southeast University

[2] School of Communication and Control Engineering, Jiangnan University

[3] School of Instrument Science and Engineering, Southeast University

来源：

Gaojishu Tongxin/Chinese High Technology Letters | 2010年 / 20卷 / 06期

关键词：

Adaptive estimation; AR model; Non-Unear filtering; Silicon micro-machined gyroscope; Unscented Kalman filtering (UKF);

D O I：

10.3772/j.issn.1002-0470.2010.06.013

中图分类号：

学科分类号：

摘要：

Considering that the silicon micro-machined gyroscope bias is often non-repeatable and is easily affected by some nonlinear factors, the paper suggests that the bias is AR modeled by a really-time recursive method, the state equation and measurement equation are built, and an adaptive unscented Kalman filtering (AUKF) is devised to filter the gyroscope bias by means of the variance inflation. Based on the Unear measuring procedure, a simplified UKF algorithm is used to really-time improve the procession. The simulation from the gyroscope static bias and the dynamic measurement shows that the simplified AUKF algorithm is evidently superior to the UKF and the Kalman filtering (KF). The simplified AUKF can increase the bias stability 3 times and 2 times of the KF and the UKF. The decreased error between the filtered and unfiltered dynamic measurement is 1.46 and 1.34 times of the UKF and KF. The mean of the dynamic measurement does not change. Compared with the KF and the UKF, the AUKF occupies the most processing time.

引用

页码：623 / 627

页数：4

共 14 条

[1]

20, 7, pp. 1555-1557, (2007)

[2]

Broersen P.M.T., Waele S.D., Automatic identification of time-series models from long autoregressive models, IEEE Trans on Instrumentation and Measurement, 54, 5, pp. 1862-1868, (2005)

[3]

17, 8, pp. 1845-1847, (2005)

[4]

35, 6, pp. 929-931, (2006)

[5]

Shin E.H., El-Sheimy N., An unscented Kalman filter for in-Motion alignment of low-cost IMUs, Proceedings of the Position Location and Navigation Symposium, pp. 273-279, (2004)

[6]

20, 5, pp. 481-490, (2005)

[7]

33, 3, pp. 184-187, (2007)

[8]

28, 6, pp. 505-509, (2008)

[9]

Yang Y.X., Gao W.G., A new learning statistic for adaptive filter based on predicted residuals, Progress in Natural Science, 16, 8, pp. 833-837, (2006)

[10]

Yang Y.X., Gao W.G., An optimal adaptive Kalman filter, Journal of Geodesy, 80, 4, pp. 177-183, (2006)

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