KEYWORDS: Fiber optic gyroscopes, Signal detection, Signal processing, Design, Optical circuits, Gyroscopes, Field programmable gate arrays, Digital signal processing, Miniaturization, Sensors
The signal processing circuit of the fiber optic gyroscope adopts a digital closed-loop processing scheme. It modulates the Y-waveguide integrated optical device through a D/A converter and its amplification circuit and demodulates the optical signals containing angular rate information via the detector signal processing circuit and A/D converter. The angular rate sensed by the fiber optic gyroscope is then calculated by a digital signal processing logic chip FPGA. Traditional fiber optic gyroscopes predominantly use imported components such as operational amplifiers, FPGAs, and A/D and D/A devices. In response to the urgent demand for gyroscopes with specific sizes and 100% domestic production, and with the gradual maturity of domestic integrated electronic components, the optimized design of the main control signal processing board, component selection, and the comprehensive application of flexible lamination technology have ensured that the accuracy and performance of the fiber optic gyroscope are maintained even with full domestic circuit production and reduced size.
Fiber optic gyroscope is an inertial measurement device, has been used in aviation, aerospace and navigation fields widely. It’s easy affected by the vibration noise in the surrounding environment and the application scene. The vibration causes the stress of the fiber ring to change, pigtail vibration of the optical device and the structure resonance of the fiber gyroscope, These changes will affect the performance indexes of the fiber gyroscope, such as zero bias and zero bias stability. Filter method is added to the data processing of fiber optic gyro in paper, and the vibration noise data is filtered by adaptive adjustment of filter parameters. To a certain extent, the influence of vibration and noise on the performance of fiber optic gyroscope is reduced, the zero bias drift is reduced, and the zero bias stability of fiber optic gyroscope is improved after filtering.
With the rapid development of railway transportation in China, Inspection and maintenance of railways are becoming more important. It is of great practical significance to realize the measurement of track geometric parameters efficiently and quickly for guiding fine tuning and railway maintenance. Aiming at the problems of low efficiency and high labor cost in traditional orbit detection, a fiber optic gyro inertial navigation system is proposed to measure the geometric parameters of orbit,so as to realize the efficient, continuous and accurate measurement of track parameters. Aiming at the application scene of railway track measurement, the fiber optic gyro inertial navigation system is installed on the railway track detection vehicle. In order to reduce the influence of accumulated error of inertial navigation system, the combination of inertial navigation system and odometer is adopted. Then the geometric parameters such as track direction and longitudinal of the track are calculated. In this paper, the updating algorithm of orbit attitude position is deduced, the calibration method of installation error in orbit detection scene is given, and the actual orbit test is carried out.
Based on the track parameters measured by the total station, according to the position output by the system, the track direction and longitudinal value of 10m chord length is calculated and compared with the reference value. The track test experiment shows that, Excluding the measurement error of the total station itself, the error accuracy of the track parameters calculated by the system is less than lmm and the repeatability is less than 0.5 mm. with the traditional measurement method, this method effectively improves the relative measurement efficiency of the railway and lays the foundation for the precise inspection of the railway.
In the process of using the wavelet domain multi-scale data fusion algorithm to fuse MEMS gyroscope signals, because the gyroscope signal contains a trend term, the dynamic data of the gyroscope, especially when it contains jump data, causes the fusion value to be delayed compared to the original data of the gyroscope. In order to solve these problems, a new MEMS gyroscope data fusion method switching scheme is proposed, first using the sliding average method to extract the trend term of the gyroscope signal, and then performing the fusion processing on the gyroscope signal after removing the trend, using the cumulative and control chart algorithm and the preset threshold is used to determine whether the gyro signal is transitioned as a switching condition of the fusion method. If there is a transition, the orthogonal basis neural network data fusion algorithm is used to perform fusion processing on the collected gyro data. The experimental results show that after using this scheme, the problems caused by the original single-scale multi-scale data fusion in the wavelet domain are solved, the variance is reduced to 0.00351 in the constant rate experiment, and the effect is also significant in the jump experiment, the purpose of improving the overall output accuracy of the MEMS gyroscope during the measurement process is proved, and the effectiveness of the scheme is proved.
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