Abstract
Fiber optic gyroscope (FOG) is a core component in modern inertial technology. However, the precision and performance of FOG will be degraded by environmental drift, especially in complex temperature environment. As the modeling performance is affected by the noises in the output data of FOG, an improved wavelet threshold value based on Allan variance and Classical variance is proposed for discrete wavelet analysis to decompose the temperature drift trend item and noise items. Firstly, the relationship of Allan variance and Classical variance is introduced by analyzing the drawback of traditional wavelet threshold. Secondly, an improved threshold is put forward based on Allan variance and Classical variance which overcomes the shortcoming of traditional wavelet threshold method. Finally, the innovative threshold algorithm is experimentally evaluated on FOG. The mathematical evaluation results show that the new method can get better signal-to-noise ratio (SNR) and gain the reconstruction signal of the higher correlation coefficient (CC). As an experimental validation, the nonlinear capability of error back propagation neural network (BP neural network) is used to fit the drift trend item and find out the complex relationship between the FOG drift and temperature, and the final processing results indicate that the new denoising method can get better root of mean square error (MSE).
Highlights
Fiber optic gyroscope (FOG) has been widely used in inertial navigation system (INS), which was first proposed and demonstrated by Vail and Shorthill in 1976
The basic idea of this method is that the improved threshold is selected as the dividing line between the temperature trend item and other noise signals, avoiding the phenomenon of wavelet coefficients of the “overkill” and “overreservation.” The experimental FOG signal is processed by the improved threshold and the results show that the proposed method effectively avoids the deficiency above
In order to verify the reliability of the improved threshold for wavelet denoising method, the original signal is acquired from a group of FOGs, which are fixed on an approximate horizontal stationary platform with their sensitive axes in the vertical direction as Figure 2
Summary
Fiber optic gyroscope (FOG) has been widely used in inertial navigation system (INS), which was first proposed and demonstrated by Vail and Shorthill in 1976. FOG has significant advantages, such as no moving parts, short warming up time, low power consumption, impact resistance, accuracy wide coverage, and large dynamic range [1,2,3]. There are two main methods to avoid temperature drift error: one is employing machining techniques and experimental approaches to control temperature [5,6,7,8]; the other is modeling compensation method [3, 4, 9, 10]. The temperature modeling and compensation method is a kind of mathematical approach, which can enhance the precision of FOG by establishing an error model based on the FOG’s temperature characteristics without extra hardware cost. The temperature drift trend item is generally polluted by noises and it affects the modeling compensation accuracy
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