A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources.

Linliang Miao, Tianyi Zhang, Chao Zuo, Zijie Chen, Xiaofei Yang, Jun Ouyang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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.

Magnetic targets positioning method based on multi-strategy improved Grey Wolf optimizer.Scientific reports · 2025
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

6 authors.

Linliang MiaoSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0003-3404-8996

Tianyi ZhangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China.

Chao ZuoHubei Key Laboratory of Marine Electromagnetic Detection and Control, Wuhan 430064, China.

Zijie ChenSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China.

Xiaofei YangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China.

Jun OuyangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China.

Funding

National Natural Science Foundation of China U2141236

6 · The paper itself

Abstract

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms.

Indexed as

magnetic localizationmulti-target localizationsuper resolutiontrust region reflective

Identifiers

PMID38794081

DOI10.3390/s24103226

PMCPMC11125228

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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.