As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
本文从理论上分析了光纤涂覆层对长周期光纤光栅(long period fiber grating,LPFG)温度灵敏度的影响。根据数值分析法对有、无涂覆层的LPFG透射谱建立仿真模型,对LPFG进行温度传感的模式耦合过程进行分析。结果表明:包层模阶数越大,涂...本文从理论上分析了光纤涂覆层对长周期光纤光栅(long period fiber grating,LPFG)温度灵敏度的影响。根据数值分析法对有、无涂覆层的LPFG透射谱建立仿真模型,对LPFG进行温度传感的模式耦合过程进行分析。结果表明:包层模阶数越大,涂覆层对不同包层模有效折射率的影响越大,进而推断出不同包层模耦合的LPFG谐振峰具有不同的温度灵敏度。仿真结果验证了带涂覆层LPFG中高阶包层模耦合的谐振峰对温度更敏感,具有0.977 nm/℃的高灵敏度,是普通LPFG的10倍左右。涂覆层的存在不仅能保护光纤、提高其机械强度,更重要的是,对于高阶包层模耦合出来的透射峰,涂覆层能够有效提高它的温度灵敏度。同时,该结果对保留涂覆层制备光栅以及聚合物涂覆栅型结构方面的实验研究具有一定的参考意义。展开更多
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
文摘本文从理论上分析了光纤涂覆层对长周期光纤光栅(long period fiber grating,LPFG)温度灵敏度的影响。根据数值分析法对有、无涂覆层的LPFG透射谱建立仿真模型,对LPFG进行温度传感的模式耦合过程进行分析。结果表明:包层模阶数越大,涂覆层对不同包层模有效折射率的影响越大,进而推断出不同包层模耦合的LPFG谐振峰具有不同的温度灵敏度。仿真结果验证了带涂覆层LPFG中高阶包层模耦合的谐振峰对温度更敏感,具有0.977 nm/℃的高灵敏度,是普通LPFG的10倍左右。涂覆层的存在不仅能保护光纤、提高其机械强度,更重要的是,对于高阶包层模耦合出来的透射峰,涂覆层能够有效提高它的温度灵敏度。同时,该结果对保留涂覆层制备光栅以及聚合物涂覆栅型结构方面的实验研究具有一定的参考意义。
