Abstract
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.
Highlights
With the development of fiber optical technology, the fiber optic gyroscope (FOG) is gradually replacing other types of gyroscopes with its unique advantages, and it has become mainstream in inertial navigation system applications [1]
The results show that the improved auto regressive (AR)(3) model can fit the FOG random drift well, so the improved
Analysis become stable in about s, and the estimated values are as follows: become stable in about 30 s, and the estimated values are as follows: Under static conditions, we record the real-time data of a single axis (x-axis) FOG for 2 h
Summary
With the development of fiber optical technology, the fiber optic gyroscope (FOG) is gradually replacing other types of gyroscopes with its unique advantages, and it has become mainstream in inertial navigation system applications [1]. Much research work in modeling FOG random drift is being carried out at home and abroad. The traditional modeling of FOG random drift is limited to an offline form, which uses a random drift model based on the output data of a single gyroscope obtained in the laboratory. The estimated model is of certain significance in analyzing the characteristic of random drift due to changeable measurement conditions under the circumstances of the moment, such as temperature, humidity, electromagnetic field and gravity, as well as the impact caused by restarting the gyroscope; the reliability of a model established offline will decrease, and its universality is not strong [3,4]. To solve the above problems, we need to develop a real-time filtering method of online modeling of FOG random drift, based on real-time observations at each restart of the gyroscope, so an online model of FOG random drift can be established with a software method, and the model is used to realize real-time filtering on the FOG
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