Transient Nanostrain Detection in Phi-OTDR Using Statistics-Based Signal Processing

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is capable of detecting acoustic emission induced small strain with high sensitivity. However, there is a tradeoff between sensitivity, bandwidth and detection range, which makes the detection of a transient weak signal challenging. In this work, we focus on transient weak signal detection using Φ-OTDR. To achieve this aim, we propose a cascaded statistics-based signal-processing framework in a Φ-OTDR system to fetch the transient weak signal from a noisy background. Our framework is based on two key elements, including an estimator that is derived based on the probability characteristic of the Rayleigh backscattered light, and a time-frequency feature extraction process that maps the signal to the time-frequency domain. Using our statistics-based signal processing Φ-OTDR, we demonstrate experimentally the detections of, firstly a weak persistent signal with a magnitude down to 4 nε and a frequency up to 40 kHz, and then a weak transient acoustic tone-burst signal. Our proposed scheme is promising for Φ-OTDR performance improvement particularly in weak signal detections, and it will find new applications in the future systems.