Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911011547643904 |
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| author | Wang, Linlin Wang, Wei Wang, Dezhao Wang, Shanwen |
| author_facet | Wang, Linlin Wang, Wei Wang, Dezhao Wang, Shanwen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks Wang, Linlin Wang, Wei Wang, Dezhao Wang, Shanwen Signal Processing 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. |
| title | Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.15125 |