Project Description:
Optical fibre networks are critical to global connectivity, yet they are susceptible to accidental damage or deliberate tampering. While distributed acoustic sensing (DAS) has been widely investigated for disturbance detection, an alternative approach relies on monitoring the state of polarisation (SOP) of light propagating in the fibre. The SOP is highly sensitive to mechanical perturbations, bends, and vibrations, and therefore provides a powerful, low-intrusion method to detect physical disturbances.
By continuously tracking SOP variations, it becomes possible to identify unusual events that may signal attempts to disturb or access the fibre, such as digging, tapping, or intentional tampering. Machine learning techniques can be employed to learn the characteristic temporal and statistical patterns of SOP variations, enabling reliable detection of anomalous events.
Goal:
The goal of this project is to design and implement algorithms that exploit SOP measurements to detect and classify fibre disturbances. The student will focus on advanced signal processing, feature extraction, and anomaly detection methods tailored to polarisation dynamics. Access to experimental data from controlled disturbances, as well as real fibre deployments, will be provided to support model training and validation.