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
相关论文
共 25 条
  • [1] WANG Chao, Research on modelling, analysis and compensation of fiber optic gyroscope random drift, (2015)
  • [2] HUANG Lei, Auto regressive moving average (ARMA) modeling method for gyro random noise using a robust Kalman filter, Sensors, 15, 10, pp. 25277-25286, (2015)
  • [3] CHEN Xiyuan, Modeling random gyro drift by time series neural networks and by traditional method, Proceedings of 2003 International Conference on Neural Networks and Signal Processing, 1, pp. 810-813, (2003)
  • [4] ZHU Kuibao, ZHANG Chunxi, SONG Ningfang, Modeling and identification of random drift for FOG, Journal of Beijing University of Aeronautics and Astronautics, 32, 11, pp. 1354-1357, (2006)
  • [5] TANG Xiaohong, ZHAO Luyang, LI Luming, Modified adaptive filtering algorithm for MEMS gyroscope random noise, Transducer and Microsystem Technologies, 37, 10, pp. 133-136, (2018)
  • [6] BAI Junqing, ZHANG Ke, WEI Yuxin, Modeling and analysis of fiber optic gyroscope random drifts, Journal of Chinese Inertial Technology, 20, 5, pp. 621-624, (2012)
  • [7] FU Jun, HAN Hongxiang, Modified adaptive Real-time filtering algorithm for MEMS gyroscope random noise, Acta Photonica Sinica, 48, 12, pp. 183-191, (2019)
  • [8] WU Fumei, YANG Yuanxi, Gyroscope random drift model based on the higher-order AR model, Acta Geodaetica et Cartographica Sinica, 36, 4, pp. 389-394, (2007)
  • [9] LI Jialei, XU Hualong, HE Jing, Real-time filtering methods of random drift of fiber optic gyroscope, Journal of Astronautics, 31, 12, pp. 2717-2721, (2010)
  • [10] ZENG Qinghua, HUANG Lei, LIU Jianye, Et al., Real-time filtering methods of FOG random noise based on ARMA model, Journal of Chinese Inertial Technology, 23, 1, pp. 120-124, (2015)
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