Error Of Fiber Optic Gyroscope Research Articles

Temperature error compensation method of fiber optic gyroscope based on deep learning

This work provides an error compensation strategy based on deep learning to address the temperature error of a fiber optic gyroscope (FOG). The Attention structure was used to improve the Long Short-Term Memory Network (LSTM), and the improved network was utilized to establish the prediction and compensation model of FOG error. From the two perspectives of the learning ability of the neural network and the improvement of FOG performance, this paper selects three indicators: prediction accuracy, mean square error, and FOG bias stability to comprehensively evaluate the performance of the compensation model. Through comparison, the compensation effect is significantly enhanced.

Analysis of fiber optic gyroscope dynamic error based on CEEMDAN

The fiber optic gyroscope (FOG) is an angular rate sensor based on Sagnac effect. It has been widely used in aviation, unmanned, robot, and other fields due to its benefits of all solid state, high precision, and low cost. However, the photoelectric devices inside the FOG will produce a non-negligible error in the dynamic environment, resulting in zero bias drift and increased noise. Currently, research on the dynamic error of FOG focuses primarily on linear vibration, assuming that no input angular rate exists at this moment. However, the dynamic errors caused by angular vibration are more complicated in the practical application environment. The output of FOG includes both effective angular information and dynamic error at this moment, which means that the dynamic error should be removed while the effective angular information must be retained. In this paper, we focus on the effect of angular vibration on FOG, and propose a dynamic error compensation method of FOG based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) that properly separates the effective signal and dynamic error. We also consider the amplitude frequency characteristics of FOG under dynamic conditions and utilize the FOG bandwidth test experiment to verify the effectiveness of our method. The experimental results show that the compensated signal can correctly calculate the bandwidth of FOG. It indicates that our method can effectively compensate the dynamic error in angular vibration environment while improving the performance of FOG in dynamic environment.

Method of reducing thermal-induced errors of a fiber optic gyroscope by adding additional winding layers.

Thermal-induced errors have a significant impact on the environmental adaptability of a fiber optic gyroscope (FOG). Reasonable winding methods can reduce and offset the thermal-induced errors. However, complex methods put higher requirements on winding accuracy. By adding additional winding layers on the outer surface of the fiber coil, an improved winding method to reduce the temperature error of FOG is proposed in this paper. Simulations in temperature-control conditions and time-varying temperature conditions are performed. Simulation and experimental results show that additional winding layers lead to a satisfactory reduction of thermal-induced rate errors. With parameter estimation and error compensation, thermal-induced errors can be further reduced.

Error of fiber-optic gyroscope at angular oscillations

Error of fiber-optic gyroscope at angular oscillations

Transverse Magneto-Optic Error of a Miniature Solid-Core Photonic-Crystal Fiber Optic Gyroscope

The transverse magneto-optic error is a significant nonreciprocal error of fiber optic gyroscopes, particularly for the miniature fiber optic gyroscope. In this paper, the transverse magneto-optic error of a solid-core photonic-crystal fiber optic gyroscope is theoretically analyzed and experimentally measured, as well as compared with that of the conventional fiber optic gyroscopes. The results show that the transverse magneto-optic error per turn of the miniature photonic-crystal fiber optic gyroscope is ~ 3.5 times smaller than that of the conventional fiber optic gyroscopes. A thinner $\mu $ -metal shield can be used in a miniature photonic-crystal fiber optic gyroscope, resulting in less cost and weight. Meanwhile, the experimental and the calculated results agree well with each other, which illustrates that the transverse magneto-optic error derives mainly from the bend-induced asymmetry distribution of the refractive index in fiber. Considering the remarkable properties of the solid-core photonic-crystal fibers, they are the good candidates for the future miniature fiber optic gyroscopes.

Integrated modeling and filtering of fiber optic gyroscope's random errors

In order to analyze and process the random error of the fiber optic gyroscope (FOG) and improve its use precision, an error modeling method that combined empirical mode decomposition (EMD) and time series model was proposed. On the basis of the intrinsic mode functions (Imf) which was obtained by empirical mode decomposition, auto-regressive and moving average model (ARMA) modeling is performed hierarchically for each Imf. Then, Kalman filtering is performed layer by layer on the basis of the model to remove the random drift signals from the real angular velocity information. At the end of the algorithm, the signal which had been filtered need to be reorganized, and through the above steps, the conception of analyzing and modeling in connection with the random error of FOG from full frequency's point of view was realized. Compared with other modeling methods, this method has a higher degree of simulation matching to the original data, at the same time, the experimental results have further shown that this method can effectively remove the signal of random drift from the fiber optic gyroscope's output signal and improve its use precision significantly.

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

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.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

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.

基于自适应窗长的光纤陀螺随机误差实时分析方法

基于自适应窗长的光纤陀螺随机误差实时分析方法

A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes’ pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

Vibration Error Research of Fiber Optic Gyroscope in Engineering Surveying

In the process of the engineering surveying by FOG (fiber optic gyroscope), there are a lot of measurement error caused by many kinds of factors, vibration error is one of them. Analyzing the output signal of FOG on the effect of vibration, Kalman filter can be used to inhibit the drift of output signal, and it is experimentally validated based on the filtering algorithm. The results show that, the vibration error of FOG was reduced, and the validity of the method has been proved. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2367

Fiber optic gyroscope vibration error due to fiber tail length asymmetry based on elastic-optic effect

In the assembly of fiber optic gyroscope (FOG), the summation of the fiber tail length of waveguide and fiber ring in two directions cannot be exactly equal (i.e., fiber tail length of FOG in two directions is asymmetry). We analyze and calculate the vibration error of FOG due to this fiber tail length asymmetry based on elastic-optic effect. Inertial navigation simulation is performed to show attitude and positioning errors caused by this vibration error, which clearly shows the importance of reducing fiber-tail length asymmetry in high-accuracy applications.

The Research of Fiber Optic Gyroscope Error Based on Insertion Loss and Beam-Splitting Ratio of Y-Waveguide

Y-waveguide is widely used in the integrated optics,among which the beam-splitting ratio and insertion loss are the most critical parameters. The measurement setups and methods for the two parameters are introduced. The temperature is the most important factor to the stability of Y-waveguide. As for the insertion loss, the measurement setup was built up based on the measurement principle, and the factors which affected the measurement of the insertion loss were analyzed. As for the beam-splitting ratio, the output luminous power for two tail fibers of Y-waveguide was measured respectively. In different temperature condition, we study the error of FOG by various beam-splitting ratio after working out the beam-splitting ratio according to the formula. Then put into compensation implement.

A New Dynamic Test Method of Fiber Optic Gyroscope

The sway motion of ships follows sine law. Therefore, it needs high dynamic performance of fiber optic gyroscope (FOG) to meet the serious working environment. A new test method was introduced to make reasonable assessment on the dynamic performance of FOG. The principle of this new test method was that the sine wave provided by the IP core in FPGA was superimposed on the digital ladder to simulate the input signal of sway motion. It could be proved that the serial of sine signal, which was generated by this method, was equivalent to the external input signal. Moreover, the dynamic error model of FOG was established to provide the theory of dynamic test. The results of dynamic test of FOG indicated that the dynamic error did not have divergent trends, the system parameters set was reasonable and the FOG system was stable. It also can be seen that dynamic error of FOG was increased with the increasing of sine wave amplitude, which was accord with the actual situation. The new dynamic test method of FOG was verified.