A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing

摘要

We propose a compressed sampling and dictionary learning framework forfiber-optic sensing using wavelength-tunable lasers. A redundant dictionary isgenerated from a model for the reflected sensor signal. Imperfect priorknowledge is considered in terms of uncertain local and global parameters. Toestimate a sparse representation and the dictionary parameters, we present analternating minimization algorithm that is equipped with a pre-processingroutine to handle dictionary coherence. The support of the obtained sparsesignal indicates the reflection delays, which can be used to measureimpairments along the sensing fiber. The performance is evaluated bysimulations and experimental data for a fiber sensor system with common corearchitecture.