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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.

Other conference communications or articles related to authors from the University of Cantabria

 Publisher: SPIE Society of Photo-Optical Instrumentation Engineers

 Publication date: 14/10/2019

 No. of pages: 4

 Publication type: Conference object

 DOI: 10.1117/12.2540012

 ISSN: 0277-786X,1996-756X

 Spanish project: TEC2016-76021-C2-2-R

 Publication Url: