Compensation method for temperature error of fiber optical gyroscope based on relevance vector machine.

Area of Science:

• Instrumentation and Measurement
• Machine Learning Applications
• Optical Engineering

Background:

• Fiber optical gyroscopes (FOGs) are crucial for navigation but susceptible to bias drift caused by ambient temperature fluctuations.
• Existing temperature compensation methods may not fully capture the complex temperature dependence of FOG bias.
• Improving FOG bias stability is essential for high-precision applications in dynamic environments.

Purpose of the Study:

• To propose and validate a novel temperature-compensation method for FOGs using a relevance vector machine (RVM) within a Bayesian framework.
• To enhance the bias stability of FOGs in environments with significant temperature variations.
• To compare the efficacy of the RVM method against traditional temperature modeling techniques.

Main Methods:

• A temperature-compensation model was developed utilizing the relevance vector machine (RVM) algorithm, grounded in Bayesian inference.
• The RVM model was trained and tested to interpret the temperature-dependent bias characteristics of the FOG.
• Performance was benchmarked against quadratic polynomial regression, neural networks, and support vector machine (SVM) models.

Main Results:

• The RVM-based temperature compensation method demonstrated superior accuracy in modeling FOG gyro bias compared to quadratic polynomial regression, neural networks, and SVM.
• Experimental validation confirmed a significant reduction in FOG bias instability across the tested temperature range of -40°C to 60°C.
• The proposed RVM approach effectively mitigated the impact of temperature variations on FOG performance.

Conclusions:

• The relevance vector machine (RVM) method offers a highly accurate and effective approach for compensating FOG bias drift in fluctuating temperature conditions.
• This technique substantially improves the bias stability and adaptability of FOGs for use in challenging ambient temperature environments.
• The RVM-based compensation strategy presents a promising advancement for enhancing the reliability and precision of fiber optical gyroscopes.