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

We propose a novel distributed demodulation optimization algorithm for optical frequency domain reflectometry (OFDR). This algorithm applies discrete-time analytic (DTA) signals to the Rayleigh backscattered signal (RBS) reconstruction. The DTA-RBS algorithm utilizes only positive-frequency components in the distance domain and employs a frequency-domain construction method to generate DTA-RBS, thereby improving performance without increasing the computational complexity of the OFDR demodulation algorithm. By leveraging the envelope property, DTA-RBS enhances spectral feature information and intensity while effectively suppressing high-frequency noise and spurious oscillations introduced during reconstruction, thereby maintaining a higher correlation between the reference and test data. Comprehensive experimental validation demonstrates significant performance improvements across multiple metrics. Cross-correlation intensity analysis shows that the average peak intensity of DTA-RBS reaches 0.9527, compared to 0.9096 for the conventional method. Standard deviation measurements on unstrained fiber segments demonstrate a 63% improvement. Large-strain demodulation experiments show that DTA-RBS exhibits superior strain demodulation performance and robustness, whereas the conventional method produces anomalous data points due to false peaks obscuring genuine correlation peaks. These results confirm that the DTA-RBS method provides a theoretically rigorous and practically effective approach for enhancing the sensing accuracy, stability, and robustness of OFDR in high-precision distributed measurement applications.