A Two-Dimensional (2-D) Sensor Network Architecture with Artificial Intelligence Models for the Detection of Magnetic Anomalies.

Paolo Gastaldo, Rodolfo Zunino, Alessandro Bellesi, Alessandro Carbone, Marco Gemma, Edoardo Ragusa

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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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

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0 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Paolo GastaldoDepartment of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN), University of Genoa, 16145 Genoa, Italy.ORCID 0000-0002-5748-3942

Rodolfo ZuninoDepartment of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN), University of Genoa, 16145 Genoa, Italy.ORCID 0000-0002-2150-7076

Alessandro BellesiSkytech srl, 19136 La Spezia, Italy.

Alessandro CarboneSkytech srl, 19136 La Spezia, Italy.

Marco GemmaSkytech srl, 19136 La Spezia, Italy.

Edoardo RagusaDepartment of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN), University of Genoa, 16145 Genoa, Italy.ORCID 0000-0002-5527-6325

Funding

European Union -NextGenerationEU and by the Ministry of University and Research (MUR) National Recovery and Resilience Plan (NRRP), Mission 491 4, Component 2, Investment 1.5, project "RAISE - Robotics and AI for Socio-economic Empowerment" 492 (ECS00000035)

6 · The paper itself

Abstract

The paper presents the development and preliminary evaluation of a two-dimensional (2-D) network of magnetometers for magnetic anomaly detection. The configuration significantly improves over the existing one-dimensional (1-D) architecture, as it enhances the spatial characterization of magnetic anomalies through the simultaneous acquisition of data over an extended area. This leads to a reliable estimation of the target motion parameters. Each sensor node in the network includes a custom-designed electronic system, integrating a biaxial fluxgate magnetometer that operates in null mode. Deep learning models process the raw measurements collected by the magnetometers and extract structured information that enables both automated detection and preliminary target tracking. In the experimental evaluation, a 5×5 array of nodes was deployed over a 12×12 m

Indexed as

deep learningmagnetic anomaly detectionmagnetometric networks

Identifiers

PMID41682281

DOI10.3390/s26030764

PMCPMC12899529

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.