Adaptive H∞ Kalman filter based random drift modeling and compensation method for ring laser gyroscope. (1st January 2021)

• Record Type: Journal Article
• Authors: Wang, Minlin; Dong, Xueming; Qin, Chaojun; Liu, Jingya
• Is Part Of: Measurement. Volume 167(2021)
• Publication Date: 2021-01-01
• Subjects: RLG -- Random drift -- ARMA model -- adaptive H∞ Kalman filter -- Allan variance; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108170
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: This paper proposes a random drift modeling and compensation method for RLG. This method incorporates H-infinity technique into the adaptive Kalman filter. Allan variance method is used to analyze the random drift influence on RLG. Experiment results demonstrate the effectiveness of our proposed approach. Abstract: This paper investigates the random drift influence on ring laser gyroscope (RLG) and proposes a random drift modeling and compensation method to improve the measurement accuracy of RLG. The traditional compensation method for random drift is the standard Kalman filter. However, it needs to know the accurate discrete-time state-space model and the statistic characteristics of process and measurement noises, which limits the performance of Kalman filter. Therefore, based on the autoregressive moving average (ARMA) model, this paper incorporates the H ∞ technique and adaptive law design into the standard Kalman filter to deal with the system uncertainty and estimate the statistic characteristics of unknown noises. To test the performance of our proposed filter, the Allan variance method is adopted to analyze the random drift influence under different Kalman-type filters. The experimental results have been given to demonstrate that under the proposed adaptive H ∞ Kalman filter, the influence of random drift on RLG output is largely reduced and the calibration accuracy for turntable is improved significantly.