Fiber Optic Gyro Random Error Suppression Based on Dual Adaptive Kalman Filter

Area of Science:

• Instrumentation
• Signal Processing

Background:

• Random errors in fiber optic gyros (FOGs) impact measurement accuracy.
• Time-varying error characteristics degrade traditional error suppression algorithms.

Purpose of the Study:

• To develop an improved method for calculating dynamic Allan variance in real-time.
• To enhance the performance of random error suppression algorithms for FOGs.

Main Methods:

• Proposed a recursive dynamic Allan variance calculation to overcome limitations of conventional methods.
• Developed a dual-adaptive Kalman filter integrating recursive dynamic Allan variance with Kalman filtering process variance estimation.
• Conducted static and dynamic validation experiments.

Main Results:

• The proposed recursive dynamic Allan variance method offers improved real-time performance and reduced spectral leakage.
• The dual-adaptive Kalman filter autonomously adjusts model parameters and noise variance.
• Experimental results show significant enhancement in suppressing angular random walk errors.

Conclusions:

• The novel approach effectively addresses time-varying error characteristics in FOGs.
• The proposed method provides superior random error suppression compared to existing algorithms.
• This advancement improves the overall accuracy and reliability of fiber optic gyros.