OFDR Distributed Demodulation Optimization Algorithm Using Discrete-Time Analytic Signal Backscattered Rayleigh...

Area of Science:

• Optoelectronics
• Signal Processing
• Distributed Sensing

Background:

• Optical Frequency Domain Reflectometry (OFDR) is a key technology for distributed sensing.
• Traditional OFDR demodulation methods face challenges with noise and spurious oscillations, impacting accuracy.
• Rayleigh backscattered signal (RBS) reconstruction is crucial for OFDR performance.

Purpose of the Study:

• To introduce a novel distributed demodulation optimization algorithm for OFDR.
• To improve the performance and robustness of OFDR systems using a new signal processing technique.
• To enhance spectral feature information and suppress noise in RBS reconstruction.

Main Methods:

• Application of discrete-time analytic (DTA) signals to RBS reconstruction, termed DTA-RBS.
• Utilizing only positive-frequency components in the distance domain for DTA-RBS generation.
• Employing a frequency-domain construction method for DTA-RBS, maintaining computational efficiency.

Main Results:

• DTA-RBS achieved an average cross-correlation peak intensity of 0.9527, outperforming the conventional method (0.9096).
• A 63% improvement in standard deviation was observed on unstrained fiber segments.
• DTA-RBS demonstrated superior strain demodulation performance and robustness, avoiding anomalous data points seen in conventional methods.

Conclusions:

• The DTA-RBS method offers a theoretically sound and practical approach to enhance OFDR.
• This novel algorithm significantly improves sensing accuracy, stability, and robustness.
• DTA-RBS is effective for high-precision distributed measurement applications.