Temperature compensation method using readout signals of ring laser gyroscope.

Area of Science:

• Instrumentation and Measurement
• Signal Processing
• Machine Learning Applications

Background:

• Traditional compensation methods for ring laser gyroscope (RLG) bias are ineffective with rapid bias variations.
• Accurate RLG bias estimation is critical for navigation and inertial sensing systems.
• Existing techniques struggle to maintain precision when RLG bias dynamics are complex.

Purpose of the Study:

• To propose a novel RLG bias temperature compensation method.
• To enhance the precision and stability of RLG bias measurements.
• To address the limitations of traditional methods during rapid bias changes.

Main Methods:

• Development of a compensation method utilizing RLG readout signals.
• Integration of the least squares support vector machine (LS-SVM) algorithm for bias modeling.
• Experimental validation of the proposed method's performance.

Main Results:

• The proposed readout signal-based LS-SVM method significantly improves RLG bias stability.
• Performance enhancement is evident compared to using original RLG data.
• The method demonstrates feasibility and effectiveness even with rapidly changing RLG bias.

Conclusions:

• The novel RLG bias temperature compensation method using readout signals and LS-SVM is effective.
• This approach offers a significant improvement in RLG bias stability and precision.
• The method provides a viable solution for applications requiring accurate RLG performance under dynamic conditions.