Interrogation of super-structured FBG sensors based on discrete prolate spheroidal sequences

A. Triana; D. Pastor

doi:10.1117/12.2267238

16 May 2017 Interrogation of super-structured FBG sensors based on discrete prolate spheroidal sequences

A. Triana, D. Pastor

Proceedings Volume 10231, Optical Sensors 2017; 102310H (2017) https://doi.org/10.1117/12.2267238
Event: SPIE Optics + Optoelectronics, 2017, Prague, Czech Republic

Abstract

We propose and demonstrate an interrogation method for optical sensors designed as Discrete Prolate Spheroidal Sequences (DPSS), since these devices are complex structures involving unique magnitude and phase response the demodulating method should be able to recover both magnitude and phase response in order to identify each device in the sensing network. To do so, we use a tunable, dual–wavelength source (Single Side Band modulation of the tunable laser carrier), sweeping over the working range of the sensors, the reflected signal from the sensors is sent to a Vector Network Analyzer (VNA) in which it is obtained the magnitude and phase ratio between the two reflected wavelengths. Experimental demonstration has been carried out. With a very good result for the manufacturing of DPSS structures, in the experiments, two DPSS sensors are placed in the same spectral region and one of them is temperature shifted. The correlation, phase and magnitude product, between the original sensor shape and the compounded measurement returns the central wavelength position of each one of the sensors with a proper ratio between the auto correlation peak (ACP) and the cross correlation signal (XC). In this way, feasibility of the interrogation technique for devices involving magnitude and phase distinction has been validated, not only to identify sensors in a measurement network but also to allow overlapping between them.

Citation Download Citation

A. Triana and D. Pastor "Interrogation of super-structured FBG sensors based on discrete prolate spheroidal sequences", Proc. SPIE 10231, Optical Sensors 2017, 102310H (16 May 2017); https://doi.org/10.1117/12.2267238

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