Application of a genetic algorithm Elman network in temperature drift modeling for a fiber-optic gyroscope

2014 ◽  
Vol 53 (26) ◽  
pp. 6043 ◽  
Author(s):  
Xiyuan Chen ◽  
Rui Song ◽  
Chong Shen ◽  
Hong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Fiber Optic ◽  
Fiber Optic Gyroscope ◽  
Elman Network ◽  
Temperature Drift

A Novel Compensation Method for FOG Temperature Drift Based on TUKF

2014 ◽  
Vol 568-570 ◽  
pp. 405-410
Author(s):  
Yang Li ◽  
Bai Qing Hu ◽  
Feng Zha ◽  
Kai Long Li
Keyword(s):  
Fiber Optic ◽  
Unscented Kalman Filter ◽  
Network Models ◽  
Compensation Method ◽  
Mean Square ◽  
Numerical Instability ◽  
Fiber Optic Gyroscope ◽  
Elman Network ◽  
Temperature Drift ◽  
Mean Square Errors

Aiming at the problem of modeling and compensation of the fiber optic gyroscope (FOG) drift caused by temperature, a novel compensation method for FOG temperature drift based on transformed unscented Kalman filter (TUKF) is proposed. Elman network with faster convergence speed is used to modeling and TUKF algorithm is adopted to train the weights of Elman network, which effectively solves the problem of numerical instability. The results prove that the proposed method has higher precision compared with Elman network and IUKF network models. By using the TUKF algorithm, the root mean square errors (RMSE) are improved by 60%  in temperature rise period and 50.5% in fall period.

The Compensation of Y Waveguide Temperature Drifts in FOG with the Thermal Resistor

2014 ◽  
Vol 924 ◽  
pp. 336-342 ◽  
Author(s):  
Ying Li Wang ◽  
Li Yong Ren ◽  
Jin Tao Xu ◽  
Jian Liang ◽  
Meng Hua Kang ◽  
...  
Keyword(s):  
Lithium Niobate ◽  
Fiber Optic ◽  
Driving Voltage ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift ◽  
Thermal Resistor ◽  
Bias Drift ◽  
Half Wave Voltage ◽  
Half Wave ◽  
Environment Temperature

The lithium niobate integrated optical phase modulator (Y waveguide) is the key device in the digital closed-loop fiber optic gyroscope. However, the half-wave voltage of the lithium niobate changes with the environment temperature, which produces the phase bias drift and ultimately decreases the accuracy of FOG. In this manuscript, the thermal resistor is introduced in the amplification part in the driving circuits of Y waveguide. Due to the characteristic of the thermal resistor, the magnitude of driving voltage on Y waveguide changed with temperature to compensate the electro-optic effects temperature drift of the lithium niobate. This method was proved to improve the performance of fiber optic gyroscopes conveniently in experiment.

Temperature Drift Modeling and Compensating of Fiber Optic Gyroscope

2012 ◽  
Vol 220-223 ◽  
pp. 1911-1916 ◽  
Author(s):  
Tao Xiao ◽  
Ming Hua Pan ◽  
Guo Li Zhu
Keyword(s):  
Least Squares ◽  
Temperature Change ◽  
Autoregressive Model ◽  
Fiber Optic ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift ◽  
Factors Affecting ◽  
Least Squares Fitting ◽  
Drift Model ◽  
Main Factors

The main factors affecting the temperature drift of the fiber optic gyroscope was analyzed in this paper. The autoregressive model of temperature drift related to the temperature and the rate of temperature change was built. The coefficients of the model can be obtained by least squares fitting. Experiments show that the model was effective. With the drift model the drift trend caused by temperature can be estimated. The temperature drift can be compensated using the drift trend. The experiment result shows that the drift error can be decreased about 87% after compensation.

Modeling of Static Temperature Drift for Fiber Optic Gyroscope Based on Wavelet Network

2010 ◽  
Vol 37 (12) ◽  
pp. 2980-2985
Author(s):  
李家垒 Li Jialei ◽  
许化龙 Xu Hualong ◽  
何婧 He Jing
Keyword(s):  
Fiber Optic ◽  
Wavelet Network ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift

scholarly journals A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ning Mao ◽  
Jiangning Xu ◽  
Jingshu Li ◽  
Hongyang He
Keyword(s):  
Neural Network ◽  
Fiber Optic ◽  
Short Term Memory ◽  
Moving Average ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Measurement Unit ◽  
Support Vector ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift

Fiber optic gyroscope (FOG) inertial measurement unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in positioning and navigation of military and aerospace fields, due to its simple structure, small size, and high accuracy. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. In order to reduce the FOG drift and improve the navigation accuracy, a long short-term memory recurrent neural network (LSTM-RNN) model is established, and a real-time acquisition method of the temperature change rate based on moving average is proposed. In addition, for comparative analysis, backpropagation (BP) neural network model, CART-Bagging, classification and regression tree (CART) model, and online support vector machine regression (Online-SVR) model are established to filter FOG outputs. Numerical simulation based on field test data in the range of -20°C to 50°C is employed to verify the effectiveness and superiority of the LSTM-RNN model. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. This proposed solution can be applied in military and aerospace fields.

scholarly journals An Innovative Wavelet Threshold Denoising Method for Environmental Drift of Fiber Optic Gyro

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qian Zhang ◽  
Lei Wang ◽  
Pengyu Gao ◽  
Zengjun Liu
Keyword(s):  
Neural Network ◽  
Fiber Optic ◽  
Back Propagation ◽  
Threshold Value ◽  
Allan Variance ◽  
Discrete Wavelet ◽  
Fiber Optic Gyroscope ◽  
Threshold Method ◽  
Temperature Drift ◽  
Denoising Method

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).

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