Phase tracking using a Kalman filter based on probability density distribution in frequency-scanning interferometry.

Area of Science:

• Metrology and Measurement Science
• Optical Engineering
• Laser Technology

Background:

• Frequency-scanning interferometry (FSI) with external cavity diode lasers (ECDL) is crucial for absolute distance measurement.
• ECDL scanning nonlinearity and phase noise limit FSI accuracy by affecting phase extraction.
• Non-orthogonal errors are a significant challenge in FSI precision measurements.

Purpose of the Study:

• To develop a robust algorithm for mitigating non-orthogonal errors in FSI.
• To improve the accuracy of phase extraction and absolute distance measurement in FSI systems.
• To introduce a novel data fusion strategy for enhanced FSI performance.

Main Methods:

• A composite algorithm integrating least-squares and Heydemann correction was developed.
• Kalman filtering was employed for precise phase tracking.
• A parameter selection strategy based on instantaneous frequency distribution was used for phase observation and theoretical model fusion.

Main Results:

• The proposed method effectively mitigates non-orthogonal errors.
• Experimental validation demonstrated an average phase error of 0.12%.
• Absolute distance measurements achieved a standard deviation below 1.7 µm, with positioning accuracy error of 0.29 µm.

Conclusions:

• The composite algorithm and Kalman filtering significantly enhance FSI accuracy.
• The multi-dimensional data fusion approach offers a new perspective for FSI measurements.
• The validated method shows high precision and reliability for absolute distance measurement applications.