RESUMO
We propose an efficient model-based signal processing approach for optical fiber sensing with fiber Bragg grating (FBG) arrays. A position estimation based on an estimation of distribution algorithm (EDA) and a reflectivity estimation method using a parametric transfer matrix model (TMM) are outlined in detail. The estimation algorithms are evaluated with Monte Carlo simulations and measurement data from an incoherent optical frequency domain reflectometer (iOFDR). The model-based approach outperforms conventional Fourier transform processing, especially near the spatial resolution limit, saving electrical bandwidth and measurement time. The models provide great flexibility and can be easily expanded in complexity to meet different topologies and to include prior knowledge of the sensors. Systematic errors due to crosstalk between gratings caused by multiple reflections and spectral shadowing could be further considered with the TMM to improve the performance of large-scale FBG array sensor systems.
RESUMO
The intrinsic and extrinsic coupling losses of step-index polymer optical fibers are statistically examined by Monte Carlo simulations. In contrast to most existing models that linearly scale individual losses, a comprehensive analytic coupling loss model is used that also considers the interdependencies between mismatches in numerical aperture and core diameter, as well as radial and longitudinal offsets. As a typical example, the coupling losses of A4a.2 step-index multimode fibers are analyzed for an equilibrium mode distribution. The results show considerably less conservative coupling loss estimations than with traditional models, improving link power budgeting.