Abstract

This work targets a remarkable quasi-distributed temperature sensor based on an apodized fiber Bragg grating. To achieve this, the mathematical formula for a proposed apodization function is carried out and tested. Then, an optimization parametric process required to achieve the remarkable accuracy that is based on coupled mode theory (CMT) is done. A detailed investigation for the side lobe analysis, which is a primary judgment factor, especially in quasi-distributed configuration, is investigated. A comparison between elite selection of apodization profiles (extracted from related literatures) and the proposed modified-Nuttal profile is carried out covering reflectivity peak, full width half maximum (FWHM), and side lobe analysis. The optimization process concludes that the proposed modified-Nuttal profile with a length (L) of 15 mm and refractive index modulation amplitude (Δn) of 1.4×104 is the optimum choice for single-stage and quasi-distributed temperature sensor networks. At previous values, the proposed profile achieves an acceptable reflectivity peak of 100.426  dB, acceptable FWHM of 0.0808 nm, lowest side lobe maximum (SL max) of 7.037×1012  dB, lowest side lobe average (SL avg) of 3.883×1012  dB, and lowest side lobe suppression ratio (SLSR) of 1.875×1011  dB. These optimized characteristics lead to an accurate single-stage sensor with a temperature sensitivity of 0.0136 nm/°C. For the quasi-distributed scenario, a noteworthy total isolation of 91 dB is achieved without temperature, and an isolation of 4.83 dB is achieved while applying temperature of 110°C for a five-stage temperature-sensing network. Further investigation is made proving that consistency in choosing the apodization profile in the quasi-distributed network is mandatory. If the consistency condition is violated, the proposed profile still survives with a casualty of side lobe level rise of 73.2070  dB when adding uniform apodization and 46.4823  dB when adding Gaussian apodization to the five-stage modified-Nuttall temperature-sensing network.

© 2018 Optical Society of America

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