Nonstationary dynamic stochastic error analysis of fiber optic gyroscope based on optimized Allan variance

Abstract

Fiber Optic Gyroscope (FOG) under the complicated environment exhibits nonstationary dynamic characteristics over time, and the traditional Allan variance is difficult to indicate and determine the stochastic errors. To have a better evaluation of the instability of the signal, a novel hybrid algorithm based on Overlap Nonstationary Dynamic Allan Variance (DAVAR) is proposed considering the FOG time-varying nature. The overlap sampling could make full use of entire data in every cluster and reveal the transition during the dynamic process. The estimation accuracy of the different sampling methods-based Allan Deviations are compared by simulation data and the advantages of the introduced method are verified. To track the variation of FOG error coefficients, the frequency of output signal is analyzed to determine the window length of DAVAR. Furthermore, a fast algorithm in Overlap Nonstationary DAVAR is introduced to improve calculation efficiency in processing long cluster time data. In the FOG vibration experiment, the vibration characteristics are analysed by the variations of primary error coefficients like Quantization noise. The 3-D diagrams of Overlap Nonstationary DAVAR are capable of identifying and distinguishing the dynamic stochastic error items. In conclusion, the developed algorithm has better estimation accuracy and could be utilized to analyze FOG nonstationary dynamic stochastic error systematically.