Modified Sage-Husa adaptive Kalman filter for multi-source noise mitigation in dual-comb ranging

Area of Science:

• Optics
• Signal Processing
• Metrology

Background:

• Dual optical comb ranging offers high accuracy and speed but suffers from environmental noise, reducing measurement precision.
• Time-varying and non-stationary noise corrupts measurement data in practical applications.
• Existing methods struggle to effectively mitigate multi-source noise in dual-comb systems.

Purpose of the Study:

• To develop a robust noise reduction algorithm for dual optical comb absolute distance measurement systems.
• To enhance ranging accuracy by mitigating environmental disturbances.
• To improve the real-time performance and adaptability of the filtering process.

Main Methods:

• Proposed a modified Sage-Husa adaptive Kalman filtering (SHAKF) algorithm incorporating residuals and sliding windows.
• Introduced the Variational Mode Decomposition-Singal Wavelet Transform (VMD-SWT) for digital interferometric signal processing.
• Implemented exponentially weighted updating of Q and R matrices and a sliding window strategy for adaptive parameter adjustment.

Main Results:

• The SHAKF algorithm effectively suppressed multiple noise types in dual-comb ranging.
• Ranging accuracy was improved by an order of magnitude, reaching the micrometer level.
• Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were reduced by over 63%.

Conclusions:

• The developed SHAKF and VMD-SWT algorithms provide effective multi-source noise reduction for dual optical comb ranging.
• The proposed method significantly enhances ranging accuracy and reliability in the presence of environmental disturbances.
• This advancement offers potential for more precise absolute distance measurements in various applications.