Physics-informed random forest algorithm for temperature compensation in fiber optic gyroscopes
Chenxiao Qin, Yue Liu, Rongwang Zeng, Qinghua Xu et autres
Temperature-induced bias drift severely degrades the accuracy of fiber optic gyroscopes. Existing compensation methods face a dilemma between polynomial fitting accuracy and deep learning generalization. To solve this, a physics-informed random forest (PI-RF) algorithm is proposed. By integrating a temporal memory vector …
cn (code pays fourni par la source)
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Applied Optics