Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks

Linlin Wang; Wei Wang; Dezhao Wang; Shanwen Wang

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arXiv · 2506.15125

Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks

Linlin Wang·Wei Wang·Dezhao Wang·Shanwen Wang

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Abstract

With the applicability of optical fiber-based distributed acoustic sensing (DAS) systems, effective signal processing and analysis approaches are needed to promote its popularization in the field of intelligent transportation systems (ITS). This paper presents a signal denoising algorithm using a hybrid deep-learning network (HDLNet). Without annotated data and time-consuming labeling, this self-supervised network runs in parallel, combining an autoencoder for denoising (DAE) and a long short-term memory (LSTM) for sequential processing. Additionally, a line-by-line matching algorithm for vehicle detection and tracking is introduced, thus realizing the complete processing of fiber signal denoising and feature extraction. Experiments were carried out on a self-established real highway tunnel dataset, showing that our proposed hybrid network yields more satisfactory denoising performance than Spatial-domain DAE.

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BibTeX RIS

Linlin Wang, Wei Wang, Dezhao Wang, Shanwen Wang. 2025-06-18. Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks. https://arxiv.org/abs/2506.15125

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