Distributed optical fiber sensing in coherent Φ-OTDR with a broadband chirped-pulse conversion algorithm.

We propose a broadband chirped-pulse conversion algorithm (BCPCA) to convert a finite-step scanning probe pulse into an equivalent broadband chirped probe pulse by convolving a chirp factor on the received signal in coherent phase-sensitive optical time domain reflectometry (Φ-OTDR). Combined with Rayleigh interference pattern (RIP) demodulation in chirped-pulse Φ-OTDR (CP-ΦOTDR), environmental perturbations, such as strain and temperature along the sensing fiber, can be quantitatively measured. The equivalent broadband chirped pulse is generated by digital processing, and its bandwidth can be increased by changing the composition of the scanning pulse. Thus, the measurable perturbation range of the system can be expanded. As a proof-of-concept experiment, a high-performance distributed strain measurement was realized on a 10 km fiber, the frequency response was 5 kHz, which is only limited by the fiber length, and the strain resolution was 8.04 p ε/H z. The proposed method of generating equivalent broadband chirped pulse through the digital domain can be used as a supplement to CP-ΦOTDR.

Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold.

The key point on analyzing the data stream measured by fiber optic distributed acoustic sensing (DAS) is signal activity detection separating measured signals from environmental noise. The inability to calculate the threshold for signal activity detection accurately and efficiently without affecting the measured signals is a bottleneck problem for current methods. In this article, a novel signal activity detection method with the adaptive-calculated threshold is proposed to solve the problem. With the analysis of the time-varying random noise's statistical commonality and the short-term energy (STE) of real-time data stream, the top range of the total STE distribution of the noise is found accurately for real-time data stream's ascending STE, thus the adaptive dividing level of signals and noise is obtained as the threshold. Experiments are implemented with simulated database and urban field database with complex noise. The average detection accuracies of the two databases are 97.34% and 90.94% only consuming 0.0057 s for a data stream of 10 s, which demonstrates the proposed method is accurate and high efficiency for signal activity detection.