Dispersed Fringe Sensor Piston Error Noise Resistance
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Solution Overview
Problem
Current methods for detecting piston error in dispersed fringe sensors during fine co-phasing have a poor capability to resist noise and are complex, requiring calibration of wavelength and being sensitive to pixel translation, which increases engineering complexity and cost.
Innovation Solution
A method that utilizes a two-dimensional dispersed fringe image, converting it into a one-dimensional image using superposition, and applying the Left-subtracting-right (LSR) algorithm to calculate piston error, which is then input into a closed-loop control algorithm to eliminate noise and improve co-phasing precision without requiring wavelength calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the existing method extracts piston error from one-dimensional interference image, then the detecting precision meets fine co-phasing requirements, but the capability of resisting image noise deteriorates
Solution Approach 1:
The patent transforms the one-dimensional interference image into a two-dimensional dispersed fringe image by introducing a dispersion element. This dimensional expansion allows the system to utilize both spatial dimensions for noise suppression while maintaining piston error measurement precision through the LSR algorithm applied to the dispersed fringe pattern.
2Measurement precision
If wavelength calibration is performed to improve measurement accuracy, then the piston error extraction precision is improved, but the engineering complexity and cost increase
Solution Approach 1:
The patent employs the LSR (Left-Subtract-Right) algorithm which automatically extracts piston error from the dispersed fringe image without requiring external wavelength calibration procedures. The algorithm uses the symmetric properties of the interference pattern to self-determine piston displacement, eliminating the need for complex calibration systems and reducing engineering complexity.
3Manufacturing precision
If the dispersed fringe sensor is used during fine co-phasing stage, then the co-phasing precision is improved, but the noise influence from weak illumination and camera target surface noise increases
Solution Approach 1:
The patent combines information from multiple pixels along the dispersed direction to form the final piston error measurement. By integrating signals across N pixels in the dispersion direction, the system achieves a noise reduction ratio of 1/N, effectively suppressing the impact of weak illumination and camera noise while maintaining fine co-phasing precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method significantly enhances the signal-to-noise ratio and reduces noise influence, improving noise resistance and co-phasing precision while reducing engineering complexity and cost, with a theoretical noise reduction ratio of 1/N, where N is the number of pixels along the dispersed direction.
Implementation Method 1
a dispersed fringe sensor and a processing device... collecting a two dimensional dispersed fringe image by the dispersed fringe sensor
Data Source
AI summary
The present disclosure provides a method for improving capability of resisting image noise of a co-phasing system of a dispersed fringe sensor. The method comprises the following steps: carrying out a coarse co-phasing adjustment by utilizing the dispersed fringe sensor until the coarse co-phasing is stabilized in a closed loop; collecting a two dimensional dispersed fringe image by the dispersed fringe sensor; superposing the dispersed fringe image along a dispersed direction so as to convert the two dimensional dispersed fringe image to a one dimensional image along an interferential direction; extracting peak values of the main peak, a left side lobe and a right side lobe of the one dimensional image along the interferential direction, and calculating corresponding piston error value of the image by carrying out a Left-subtracting-right LSR algorithm on these peak values.