ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization

Abstract: In this work, a deep convolutional adaptive filter is proposed to enhance the performance of a Raman based distributed temperature sensor system by the application of domain randomization methods for its training. The improvement of the signal-to-noise ratio in the Raman backscattered signals in the training process and translation to a real scenario is demonstrated. The ability of the proposed technique to reduce signal noise effectively is proved independently of the sensor configuration and without degradation of temperature accuracy or spatial resolution of these systems. Moreover, using single trace to noise reduction in the ROTDR signals accelerates the system response avoiding the employment of many averages in a unique measurement.

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Editorial: SPIE Society of Photo-Optical Instrumentation Engineers

 Fecha de publicación: 14/10/2019

Nº de páginas: 4

Tipo de publicación: Comunicación a Congreso

DOI: 10.1117/12.2540012

ISSN: 0277-786X,1996-756X

Proyecto español: TEC2016-76021-C2-2-R

Url de la publicación: https://doi.org/10.1117/12.2540012