Adaptive Kalman filter method with colored noise for fiber optic gyroscope random drift

被引：0
|
作者：
Jin K. [1 ]
Chai H. [1 ]
Su C. [1 ]
Xiang M. [1 ]
机构：
[1] Institute of Surveying and Mapping, Information Engineering University, Zhengzhou
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2022年 / 51卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Adaptive Kalman filter; ARIMA model; Colored noise; Fiber optic gyroscope; Random noise;
D O I：
10.11947/j.AGCS.2022.20200614
中图分类号：
学科分类号：
摘要：
Random noise reduces the accuracy of output seriously as an important part of fiber optic gyroscope (FOG) error. Accurate modeling and compensation of random noise is an effective way to improve the accuracy of FOG. To solve the problem that FOG random noise is complicated and to accurately analyze difficultly, and the colored noise in the ARIMA model is modeled as the state equation by using the state expansion method, the Harvey algorithm is reconstruct to whiten colored noise. At the same time, considering the uncertainty of priori noise and the coupling between states and noise caused by online update of ARIMA model, variational Bayesian adaptive filter (VBAKF) is used to correct the state and measurement noise. Experiments show that the Harvey method reduces the random noise sequence variance by 40% compared with the traditional filtering modeling method. The Harvey method combined with VBAKF reduces the sequence variance by 54%; VBAKF can better estimate the measurement noise than the dynamic Allan variance. Method in this paper can effectively suppress the effects of colored noise and random model inaccuracy in the random noise Kalman filter, and improve the accuracy of random error compensation. © 2022, Surveying and Mapping Press. All right reserved.
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页码：80 / 86
页数：6
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