Fiber Optic Gyroscope Output Research Articles

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method.

This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from -30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10-3 to 1.0132 × 10-4, the factor of bias instability (B) reduces from 1.53 × 10-2 to 1 × 10-3, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10-4 to 7.2110 × 10-6. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy.

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.

Analysis of the Influence of Residual Intensity Modulation in the Multifunction Integrated Optic Circuit on Fiber-Optic Gyroscopes Performance

The residual intensity modulation (RIM) of the multifunction integrated optic circuit (MIOC) can be classified into the linear term and the nonlinear term, which can impact the performance of the fiber-optic gyroscope (FOG). According to the generation mechanism of the RIM and the demodulation principle of the FOG, a model is established to analyze the influence of RIM on the FOG performance. Simulation shows that both the linear and the nonlinear RIM can lead to a large and sudden change in the FOG output at reset points of the feedback step wave, as well as fluctuations within the reset period. The magnitude of the sudden change at the reset point is proportional to the difference between the linear RIMs of two branches in the MIOC, and the fluctuation within the period of the feedback step wave is more dependent on the nonlinear RIM. The relationship between the FOG output and the linear and nonlinear RIMs are verified by experiments. The RIM-induced output can result in the increase of the bias error and the scale factor error of the FOG, especially at small input angular velocity. Furthermore, the application of FOG for the real-time high-speed position tracking to stabilize the attitude control platform can be limited.

The investigation of FOG output signal dependency on environment temperature at high rates of temperature change

The development of fiber-optic and electronic components has led to a significant widening of their application scope, in particular in the construction of ground vehicles navigation systems. Often, such systems have the special performance requirements under difficult temperature conditions: the environment temperature changing rate in some cases may reach several °C/min. One of the key elements of a navigation system is a fiber-optic gyroscope (FOG). The harsh environmental conditions increase the demands on production technology of the FOG, on the choice of construction materials, on the arrangement and shape of the parts, etc., however, in order to decrease temperature effects influence on the output signal of the device, the use of special algorithmic compensation methods is also required. Algorithmic compensation requires careful preparation of input data, which is associated with extensive experimental work. This article analyses the FOG temperature dependence characteristics in the wake of the designated experimental preparation for algorithmic compensation.

Dead Band Self-Test Method of Three-Self FOG INS

The scheme of three-self fiber optic gyroscope (FOG) inertial navigation system (INS) has been explored as a potential alternative to provide the desired self-calibration property, and FOG is the main component of the corresponding INS. However, the existence of FOG dead band will cause a severe INS accuracy degradation in process of gyro bias calibration and alignment accuracy. In this article, a correspondence was derived, that is, the ratio of the intersection angle between the FOG input axis and the east direction to the theoretical output of FOG is a constant value in various approximately eastward. Nevertheless, when the intersection angle changes, the ratio also would significantly change because of FOG working in the dead band. Thus, a dead band self-test method at the system level was proposed and the corresponding verification experiments were carried out. The experimental results indicate that the dead band of the $y$ -axis FOG is accurately tested as −0.0125°/h by the proposed method. In conclusion, our work opens up a new perspective regarding the self-test method of the dead band at the system level, and the innovative self-test method is effective and feasible.

A Highly Integrated Automatic Fiber Optical Gyroscope Sensing Coil Winding System

Fiber optic gyroscope (FOG) is a new type of optical sensor used to measure the rotating angular velocity. As the core component, fiber optic coil (FOC) plays a decisive role in the FOG output precision. The manufacturing level and efficiency of FOC have been main factors restricting the application and development of high precision FOG. Precision mechanical positioning method based on the photoelectric devices was researched, and high-performance fully automatic micro mechanical error compensation technique was developed. On these basis. automatic fiber distribution and winding control technology were realized. Eventually a multifunctional system was developed with fiber splitting module, fiber winding module, and calibration module. This system could accomplish automatic preparation of FOC with quadrupolar symmetric pattern according to the parameters set by user. The principle and working flow of each functional module are discussed in details. Experiments show that the system can achieve automatic speed changing, automatic reversing, constant small tension control and precise fiber arrangement. The new designed system greatly improve FOC production efficiency and is conductive to promoting high level FOG development.

Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method

Abstract The noise involved in drift signal of fiber optic gyroscope (FOG) mostly comes from electronic component, detection circuit and variable working environment. Gaussian white noise and fractional noise submerged in FOG output is difficult to be eliminated by conventional methods because of the non-stationary characteristics. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is a novel nonlinear and non-stationary signal processing method, which is exploited in FOG denoising. Lifting wavelet transform (LWT) technology is employed to combine with CEEMDAN method to expedite the computing efficiency and improve filtering accuracy, so that a hybrid CEEMDAN-LWT-based model is achieved. Comparison analysis with other filtering methods based on empirical mode decomposition (EMD) and its improved version is done. And ensemble empirical mode decomposition (EEMD) method combined with LWT considered as EEMD-LWT method is also applied as compared. Experimental analysis results show that new hybrid method outperforms other EMD-based filtering methods. The new method requires only 11.3% sifting iterations of the EEMD-LWT method. Meanwhile, the rate white noise, bias instability and quantization noise buried in FOG output signal decreases from 0 . 0031 ° / h , 0 . 0352 ° / h and 0 . 5412 ° to 0 . 0005 ° / h , 0 . 0056 ° / h and 0 . 0231 ° , respectively. Furthermore, de-trended fluctuation analysis (DFA) algorithm is employed to evaluate the effectiveness of hybrid method for the FOG signal filtering.

FOG temperature drift compensation method based on wavelet denoising and neural network

The output of fiber optic gyroscope (FOG) is easily affected by the temperature variations, so it leads to produce drift and the measurement accuracy of FOG is reduced. The traditional BP neural network is an optimization method of local search, which is easy to fall into local minimum, leading to the failure of network training. In order to optimize BP neural network, a temperature drift compensation method for FOG based on particle swarm optimization (PSO) and wavelet denoising is proposed. Firstly, the mechanism of FOG temperature drift is analyzed. Next, FOG static state test in different temperatures is finished. Finally, the FOG temperature drift model has been built by the method and compensate. The results show that the output standard deviation of FOG at different temperatures is reduced by 60.19%, and the compensation effect is better than traditional BP neural network.

Suppression of the quantization error in a fiber optic gyroscope using a double-electrode-pair multifunction integrated-optic circuit.

The measuring accuracy of the fiber optic gyroscope (FOG) for weak signals under very short sampling time is significantly impacted by the quantization error, impeding its application in high-speed measurement and real-time control. In this work, we propose and implement a double-electrode-pair multifunction integrated-optic circuit (MIOC), which contains an additional pair of short electrodes besides the conventional electrode-pair. Taking advantage of the better modulating precision of the additional electrode-pair, the digital feedback is more refined and the quantization error in the FOG output is significantly suppressed. The driving circuits and the control scheme of the proposed MIOC are specially designed for FOGs. Experimental results show that the resolution for extremely small angular rates at short smoothing times is significantly improved. This work provides the potential of the applications in high-speed measuring and controlling systems for high-precision FOGs.

Fiber optic gyroscope threshold denoising based on EMD-LWT

Fiber optic gyroscope (FOG) drift data is often submerged in various noises backgrounds. It is very difficult to compensate for modeling drift signals directly. In order to better eliminate the noise mixed in the FOG temperature drift data, a hybrid EMD-LWT filtering algorithm based on empirical mode decomposition (EMD) and lifting wavelet transform (LWT) threshold denoising was proposed for gyro signals preprocessing. Firstly, the noise signal of fiber optic gyro is decomposed by EMD, and the noise term and the mixed modal term of the intrinsic mode functions (IMF) are judged according to the information entropy. Then the noise term is de-noised by LWT and the mixed modal term is denoised by wavelet transform (WT). A static test was performed on an interferential FOG to verify the effectiveness of the algorithm and compared with WT and LWT. The experimental results show that the proposed EMD-LWT filtering algorithm has better filtering effect. After processing, the root mean square error (RMSE) of the reconstructed signal is reduced by 63%, which effectively removes the noise in the FOG output.

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.

Analysis of fiber optic gyroscope vibration error based on improved local mean decomposition and kernel principal component analysis.

The fiber optic gyroscope (FOG), one version of an all solid-state rotation sensor, has been widely used in navigation and position applications. However, the elastic-optic effect of fiber will introduce a non-negligible error in the output of FOG in a vibration and shock environment. To overcome the limitations of mechanism structure improvement methods and the traditional nonlinear analysis approaches, a hybrid algorithm of an optimized local mean decomposition-kernel principal component analysis (OLMD-KPCA) method is proposed in this paper. The vibration signal features of higher frequency components are analyzed by OLMD and their energy is calculated to take shape as the input vector of KPCA. In addition, the output data of three axis gyroscopes in an inertial measurement unit (IMU) under vibration experiment are used to validate the effectiveness and generalization ability of the proposed approach. When compared to the wavelet transform (WT), experimental results demonstrate that the OLMD-KPCA method greatly reduces the vibration noise in the FOG output. Besides, the Allan variance analysis results indicate the error coefficients could be decreased by one order of magnitude and the algorithm stability of OLMD-KPCA is proven by another two sets of data under different vibration conditions.

Suppression of Thermal Effect on Closed Loop Fiber Optic Gyroscope

Fiber Optic Gyroscope (FOG), an inertial sensor measures the angular rotation of an object based on the principle of Sagnac effect. FOG is subjected to various environmental disturbances such as temperature, vibration etc., when it is operated in real time. Temperature variation is the major factor that affects the gyro performance. In this paper, a novel compensation approach called temperature soaking method has been presented which makes the gyro insensitive to temperature. This method has been carried out for a 3-axis (Gx, Gy and Gz) FOG and its outputs (bias values) were taken at different temperature points. In order to reduce the bias drift of FOG output, this method uses linear curve fitting function to generate the bias error coefficients. The generated bias error coefficients are subtracted from the existing bias data, which in turn reduces the bias error. The bias values obtained for 3-axis gyro at temperature points from -20°C to +60°C before compensation is then compared with the bias values obtained for after compensation at same temperature points. Experimental results demonstrate that, after compensation the bias drift greatly reduced from 15 to 0°/hr approximately. This method effectively improves the stability and performance of a gyro and makes it to withstand to different environmental conditions which in turn increases the range of applications from military, navigation to space.

Modeling of Temperature Drift Based on Improved Fuzzy Inference for Fiber Optic Gyroscope

Modeling and compensation for the temperature drift is an effective method to improve the measurement precision of the fiber optic gyroscope(FOG).Due to the coupling effect of temperature and input rotation rate,the complicated nonlinearity of FOG output is aggravated.To describe the nonlinearity exactly and compensate the drift accurately,a new structure of the temperature drift compensation model is designed based on neural network and a novel identification approach based on improved fuzzy inference is proposed.According to the new determinate model structure,the fuzzy regulations of the fuzzy system are designed.The parameters of the member functions are adjusted adaptively using the training data,which is constructed by the actual measurement data.Then the compensation model is identified by fuzzy inference based on the least root square mean error criterion.The results show that the established model can obtain high accuracy,good applicability and attractive prophecy.

Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm

Based on the temperature drift characteristic of fiber optic gyroscope (FOG), a novel modeling and compensation method which integrated the artificial fish swarm algorithm (AFSA) and back-propagation (BP) neural network is proposed to improve the output accuracy of FOG and the precision of inertial navigation system. In this paper, AFSA is used to optimize the weights and threshold of BP neural network which determine precision of the model directly. In order to verify the effectiveness of the proposed algorithm, the predicted results of BP optimized by genetic algorithm (GA) and AFSA are compared and a quantitative evaluation of compensation results is analyzed by Allan variance. The comparison result illustrated the main error sources and the sinusoidal noises in the FOG output signal are reduced by about 50%. Therefore, the proposed modeling method can be used to improve the FOG precision.

A Novel Comprehensive Compensation Approach for Fiber Optic Gyroscope Temperature Drift by Gaussian Process Regression

Based on the test data of a fiber optic gyroscope(FOG) in full temperature and angular rate range, it is demonstrated that the characteristic of FOG temperature drift is nonlinear. A novel comprehensive compensation approach using Gaussian process regression is proposed to improve the full-temperature-range adaptability of FOG. This approach establishes directly the mapping relationship between the observable information (containing the temperature and the actual output) and the ideal linear output of FOG by choosing a suitable covariance function and optimizing its hyperparameters. Our approach overcomes the shortage of the traditional method which introduces twice compensation errors caused by modeling the zero bias and the scale factor respectively, so the compensation precision is improved and the compensation process is simplified. Compared with the traditional method and the least square support vector regression, the experimental results demonstrate that the model established in the current paper can reflect the characteristic of the temperature drift of FOG more accurately, and can obtain better generalization ability and higher compensation precision. The predicted root mean square error of the new model is less than 0.003(o)/s and the nonlinearity error of FOG is reduced from 10 to 10, which means the new model can effectively improve the insensitivity of FOG output to the temperature..

Wavelet-Based Method for Fog Signal Denoising

Fiber-Optic Gyroscope (FOG) has been widely used to measure the angle rate of vehicle in recent years. Being as unpredictable and unmeasured error, random drift generated from FOG instability create seriously bad influence on precision of FOG output, as well as Inertial Navigation System (INS). Although wavelet-based technique has made considerable progress in FOG signal denoising, almost all the achievements are based on off-line analysis that gives little contribution to practical application. This paper presents a revised plan on account of previous research on real time denoising, explaining the computational complexity reduction from theory. Through simulation and static FOG experiment using time-frequency analysis and Allan Variance as the performance evaluation standard, the denoising effectiveness compared to using traditional method has been proofed improving to a large extent. 

Study on temperature error processing technique for fiber optic gyroscope

Fiber optic gyroscope (FOG) has been widely used as satellite and automobile attitude sensor for its high reliability and light weight. However, environment temperature variation results in errors, which will decrease the precision of FOG. The purpose of this paper is to present an error processing technique for FOG to reduce the influence of temperature variation based on the error analysis of FOG and a set of temperature experiment results. The technique contains two stages: one is de-noising stage, a novel de-noising algorithm is proposed, which integrated the advantages of lifting wavelet transform and adaptive forward linear prediction; the other is modeling stage, which based on Elman neural network. The simulation results show that the noise of FOG's output can be eliminated effectively by using the proposed de-noising algorithm, which is called LWT–FLP algorithm; the drift of FOG's output can be compensated effectively by Elman neural network model. The final processing results indicate that the errors caused by varied temperature have been reduced effectively by the proposed error processing technique.

New technique for reducing the angle random walk at the output of fiber optic gyroscopes during alignment processes of inertial navigation systems

Aboelmagd NoureldinDave Irvine-HallidayUniversity of CalgaryDepartment of Electrical and ComputerEngineeringAlberta, Canada T2N 1N4Herb TablerInternational Downhole Equipment, Ltd.Edmonton, Alberta, CanadaMartin P. MintchevUniversity of CalgaryDepartment of Electrical and ComputerEngineeringAlberta, Canada T2N 1N4E-mail: mintchev@enel.ucalgary.caAbstract. Angle random walk (ARW) is the noise component at the out-put of a fiber optic gyroscope (FOG) and it affects the FOG short-termaccuracy. Practical applications of FOGs inside an inertial navigationsystem necessitate monitoring the Earth’s rotation rate component alongthe FOG sensitive axis to determine the initial attitude of the movingobject. The ARW increases the measurement uncertainty, thus affectingthe overall accuracy. We introduce a new filtering approach that signifi-cantly reduces the ARW at the FOG output to a level that can ensure anaccurate measurement of the Earth rotation rate. The filtering approachemploys the forward linear prediction (FLP) technique to design a tapdelay line filter and a new criterion, based on controlling the step sizeparameter, is introduced to ensure the fastest possible convergence ofthe adaptive algorithm, while keeping the minimal possible mean squareerror. The proposed FLP filter of 300 tap weights is capable of reducingthe ARW from 4.66 to 0.0694deg/h(