Evidence map›Paper›PMID 40808041›Full record

ArticleSensors (Basel, Switzerland)2025

Equivalent Self-Noise Suppression of Distributed Hydroacoustic Sensing System Using SDM Signals Based on Multi-Core Fiber.

Jiabei Wang, Hongcan Gu, Peng Wang, Gaofei Yao, Junbin Huang, Wen Liu, Dan Xu, Su Wu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jiabei WangNaval University of Engineering, Wuhan 430033, China.ORCID 0009-0007-2467-2388
Hongcan GuNaval University of Engineering, Wuhan 430033, China.
Peng WangNaval University of Engineering, Wuhan 430033, China.
Gaofei YaoNaval University of Engineering, Wuhan 430033, China.
Junbin HuangNaval University of Engineering, Wuhan 430033, China.
Wen LiuNaval University of Engineering, Wuhan 430033, China.
Dan XuNaval University of Engineering, Wuhan 430033, China.
Su WuNaval University of Engineering, Wuhan 430033, China.

Funding

JD Project of Hubei province 2023BAA013
6 · The paper itself

Abstract

To address the demand of equivalent self-noise suppression in a distributed hydroacoustic sensing system, this study proposes a method to enhance the acoustic sensitivity and signal-to-noise ratio (SNR) using space division multiplexed (SDM) technology based on multi-core fiber (MCF). Specifically, a dual-channel demodulation system for distributed acoustic sensing is designed using MCF. The responses of different cores in MCF are almost consistent under external acoustic pressure, while their self-noise is inconsistent. Accordingly, the acoustic pressure phase sensitivity (APPS) and SNR gain based on the accumulation of dual-channel signals are analyzed, which are verified by experiments. It is shown that the self-noise correlation coefficient between the two cores is 0.11, increasing the noise power by 3.46 dB. The APPS is increased by 5.97 dB re 1 rad/μPa after the accumulation of two-core signals, which is close to the theoretical value (6 dB). The equivalent self-noise is reduced by 2.54 dB. The experimental results reveal that the enhancement of acoustic pressure phase shift sensitivity and SNR can be achieved by the space division multiplexing (SDM) of multi-core signals, which is of great significance for suppressing the equivalent self-noise of the system and realizing the acoustic pressure detection of weak underwater signals.

Indexed as

distributed hydroacoustic sensingequivalent self-noise suppressionmulti-core fiber

Identifiers

PMID40808041
PMCPMC12349101

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.