Maximum-likelihood estimator for dual phase extraction in holographic moire

Area of Science:

• Optics and Photonics
• Signal Processing
• Metrology

Background:

• Holographic moire techniques are crucial for precise measurements.
• Extracting dual phase distributions is challenging due to noise and sensor inaccuracies.
• Nonsinusoidal waveforms and piezoelectric transducer (PZT) miscalibration complicate data analysis.

Purpose of the Study:

• To propose a robust maximum-likelihood (ML) method for extracting dual phase distributions in holographic moire.
• To address challenges posed by nonsinusoidal waveforms, noise, and PZT miscalibration.
• To enhance the accuracy and reliability of phase extraction in interferometric techniques.

Main Methods:

• Developed a maximum-likelihood (ML) method grounded in spectral estimation theory.
• Incorporated two piezoelectric transducers (PZTs) into the holographic moire setup.
• Employed a direct stochastic algorithm, probabilistic global search Lausanne, for ML function minimization.

Main Results:

• Successfully extracted dual phase distributions from holographic moire data.
• Demonstrated the method's efficacy in the presence of noise and nonsinusoidal waveforms.
• Showcased robustness against piezoelectric transducer (PZT) miscalibration.

Conclusions:

• The proposed ML method offers a reliable approach for dual phase extraction in holographic moire.
• The technique is suitable for complex experimental conditions, improving measurement accuracy.
• Probabilistic global search Lausanne provides an effective optimization strategy for ML estimation in this context.