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

doi:10.11947/j.AGCS.2022.20200614

Abstract

Abstract: 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.

Key words: fiber optic gyroscope, random noise, ARIMA model, colored noise, adaptive Kalman filter

CLC Number:

P227

JIN Kaidi, CHAI Hongzhou, SU Chuhan, XIANG Minzhi. Adaptive Kalman filter method with colored noise for fiber optic gyroscope random drift[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(1): 80-86.

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