Machine learning assisted fibre Bragg grating based temperature sensing - Djurhuus, M; Werzinger, Stefan; Schmauß, Bernhard; Clausen, A; Zibar, Darko (International Conference on Optical Fibre Sensors (OFS), 2018)

Sub-spatial resolution position estimation for optical fibre sensing applications - Zibar, Darko; Werzinger, Stefan; Schmauß, Bernhard (IEEE Sensors Conference, 2017)
Methods from machine learning community are employed for estimating the position of fibre Bragg gratings in an array. Using the conventional methods for position estimation, based on inverse discrete Fourier transform (IDFT), it is required that two-point spatial resolution is less than gratings' spacing. However, we show that by employing statistical inference methods in combination with adaptive gradient algorithm, it is still possible to estimate the grating positions even though this requirement is violated. No prior knowledge of the reflection coefficients is needed as the joint estimation of reflection coefficients and the positions is performed. From the practical point of view, we can demonstrate the reduction of the interrogator's bandwidth by factor of 2. The technique is demonstrated for incoherent optical frequency domain reflectometry (IOFDR). However, the approach is applicable to any other OFDR technique where bandwidth-resolution limitations of IDFT appl