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Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method
Xinwang Wang1, Ying Cui2,3,4, Huiliang Cao5
1School of Instrument Science and Engineering, Southeast University, Nanjing 210018, China.
Micromachines
|September 28, 2023
Summary
This study introduces an improved method for fiber optic gyroscope (FOG) signal denoising, significantly reducing temperature drift and improving accuracy. The enhanced multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method achieves superior performance.
Area of Science:
- * Instrumentation and Measurement Science
- * Signal Processing and Data Analysis
Background:
- * Fiber optic gyroscopes (FOGs) are crucial for navigation and orientation sensing.
- * FOG performance is significantly impacted by temperature drift and various noise sources, necessitating advanced signal processing techniques.
- * Existing denoising methods often struggle to effectively address complex noise patterns and temperature-induced errors in FOG signals.
Purpose of the Study:
- * To develop and validate an improved signal processing method for enhancing FOG accuracy.
- * To effectively denoise FOG output signals by addressing mixed noise and temperature drift.
- * To improve the overall performance and reliability of FOGs in varying temperature conditions.
Main Methods:
- * Utilized multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) for signal decomposition into intrinsic mode functions (IMFs).
- * Employed adaptive Kalman filter (AKF) for denoising mixed noise components.
- * Established a fiber gyroscope temperature compensation model using grey wolf optimizer-least squares support vector machine (GWO-LSSVM) to mitigate temperature drift.
Main Results:
- * The proposed MPE-CEEMDAN method, combined with AKF and GWO-LSSVM, significantly reduced FOG temperature drift across a range of -30 °C to 60 °C.
- * Quantization noise (Q) factor reduced from 6.1269 × 10-3 to 1.0132 × 10-4.
- * Bias instability (B) reduced from 1.53 × 10-2 to 1 × 10-3, and random walk of angular velocity (N) reduced from 7.8034 × 10-4 to 7.2110 × 10-6.
Conclusions:
- * The improved MPE-CEEMDAN-based method effectively denoises FOG output signals, leading to enhanced accuracy.
- * The integration of AKF and GWO-LSSVM provides a robust solution for mitigating complex noise and temperature drift in FOGs.
- * The developed algorithm offers a promising approach for improving the performance and reliability of fiber optic gyroscopes in demanding applications.
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