Polarization self-compensation in a laser-driven interferometric fiber optic gyroscope with high long-term stability

Area of Science:

• Photonics and Optical Engineering
• Inertial Navigation Systems
• Machine Learning Applications

Background:

• Fiber optic gyroscopes (IFOGs) are crucial for inertial navigation but suffer from polarization-induced drift.
• Maintaining scale-factor stability and high sensitivity in IFOGs remains a challenge.

Purpose of the Study:

• To present a novel laser-driven IFOG with polarization self-compensation.
• To enhance scale-factor stability, sensitivity, and long-term operational stability.
• To meet the stringent requirements of real-time and robust inertial navigation.

Main Methods:

• Utilized coherent light with a 200kHz linewidth for stable scale factor.
• Implemented an optical scheme ensuring polarization reciprocity and optimal working point.
• Developed a hybrid machine learning loop (MLL) combining PID and ANN for dynamic polarization drift compensation via a liquid crystal rotator (LCR).

Main Results:

• Achieved high scale-factor stability and sensitivity.
• The MLL method significantly optimized bias instability (BI) from 0.6723°/h (PID) to 0.3869°/h (MLL) at 200s.
• Performance approached the Sagnac interferometric limit (SIL) in an open environment.

Conclusions:

• The developed IFOG demonstrates superior long-term stability and accuracy.
• The hybrid MLL effectively compensates for polarization coupling drift.
• This IFOG technology is suitable for demanding real-time inertial navigation applications.