Evidence map›Paper›PMID 40730591›Full record

ArticleScientific reports2025

Method for extracting ship shaft rate features by fusing acoustic and magnetic field.

Taotao Xie, Qing Ji, Peng Yu, Jiawei Zhang

Abstract read
In one paragraph

Article in Scientific reports, 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. Article
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

4 authors.

Taotao XieNaval University of Engineering, Wuhan, China.
Qing JiNaval University of Engineering, Wuhan, China. jiumingya886@163.com.
Peng YuNaval Petty Officer Academy, Bengbu, China. 15527176227@163.com.
Jiawei ZhangNaval University of Engineering, Wuhan, China.

Funding

National Key Research and Development Program of China 2024YFF0727000
6 · The paper itself

Abstract

To address the challenge of detecting small underwater targets, this paper proposes a detection method based on the fusion of acoustic and magnetic fields. The shaft-rate acoustic field and shaft-rate magnetic field of a vessel are both closely related to the rotation of its propeller and contain rich target characteristic information. Using the vessel's shaft-rate information as a key criterion, this paper proposes to fuse the acoustic and magnetic fields to extract the shaft-rate features of the vessel. Specifically, the line spectra of the shaft-rate acoustic and magnetic fields are first extracted using DEMON spectral analysis and power spectral analysis methods, respectively. Subsequently, the extracted line spectra are fused and purified. Finally, the shaft-rate features are extracted based on the greatest common divisor (GCD) method. To verify the effectiveness of the proposed method, real-measured acoustic and magnetic data from multiple vessels were used for experimental validation. The results show that there is a significant frequency correspondence between the line spectra of the shaft-rate acoustic and magnetic fields of the same vessel. By fusing the shaft-rate related line spectra of the acoustic and magnetic fields, the problem of line-spectrum loss in a single physical field, caused by environmental noise and other factors, can be effectively compensated. Moreover, after fusing the acoustic and magnetic fields, the accuracy and stability of the vessel's shaft-rate estimation are significantly better than those of a single physical field, allowing for more reliable extraction of shaft-rate information and enhancing the basis for target identification.

Indexed as

Fused extractionShaft-rate featureShip’s acoustic fieldShip’s magnetic field

Identifiers

PMID40730591
PMCPMC12307865

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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.