Accuracy improvement of two-dimensional shape reconstruction based on OFDR using first-order differential local...

Area of Science:

• Optoelectronics
• Optical Sensing
• Metrology

Background:

• Two-dimensional (2D) shape reconstruction accuracy is compromised by spurious peaks in strain data obtained via optical frequency domain reflectometry (OFDR).
• These artifacts, often referred to as fake peaks, introduce significant errors in reconstructed shapes.
• Existing methods lack effective strategies to mitigate these strain distribution anomalies.

Purpose of the Study:

• To propose and validate a post-processing method for enhancing 2D shape reconstruction accuracy in OFDR systems.
• To suppress fake peaks in strain distribution data.
• To improve the reliability and precision of reconstructed 2D shapes.

Main Methods:

• Development of a post-processing technique utilizing first-order differential local filtering.
• Analysis of 2D shape reconstruction principles and error sources, including fake peaks.
• Simulation-based verification of the filtering method's feasibility.
• Experimental implementation using a custom OFDR 2D shape reconstruction system.

Main Results:

• Experimental validation on up bending, down bending, and arch bending configurations.
• Significant reduction in end errors for shape reconstruction: from 2.33% to 0.25% (up bending), 2.97% to 0.78% (down bending), and 1.07% to 0.20% (arch bending) over 0.5 m.
• Demonstrated effectiveness of first-order differential local filtering in suppressing fake peaks.

Conclusions:

• First-order differential local filtering is an effective post-processing method to improve OFDR-based 2D shape reconstruction accuracy.
• The proposed method successfully mitigates errors caused by fake peaks in strain data.
• This technique offers a practical solution for enhancing the precision of 2D shape reconstruction in various bending scenarios.