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Advances in Magnetic Anomaly Sensing Systems
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Advances in Magnetic Anomaly Sensing Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 3172

Special Issue Editor

Prof. Dr. Qi Han

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China
Interests: magnetic anomaly sensing; signal processing

Special Issue Information

Dear Colleagues,

In recent years, with the development of high-precision magnetometer technology and UAV technology, coupled with the rapid development of artificial intelligence technology, more and more new magnetic anomaly sensing systems and magnetic anomaly sensing algorithms have been developed. Magnetic anomaly sensing systems show a trend of unmanned, miniaturized and intelligent. At the same time, new application scenarios such as biological magnetic field measurement are constantly expanding the application scenarios and scientific connotation of magnetic anomaly sensing system. The application of magnetic anomaly sensing system not only includes the traditional geophysical exploration, magnetic anomaly target detection, buried object detection, archaeological exploration and other fields, but also extends to the fields of physical medicine, vehicle navigation, SLAM and so on.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of magnetic anomaly sensing systems.

  • Potential topics include but are not limited to:
  • Aeromagnetic anomaly sensing system
  • Underwater magnetic anomaly sensing system
  • Biological magnetic field measurement and it’s medical applications
  • Advanced magnetic sensor technology
  • Magnetic interference compensation technology
  • Advanced magnetic detection platform
  • Magnetic data processing technology
  • Magnetic anomaly sensing technology based on artificial intelligence
  • New application of magnetic anomaly sensing technology

Prof. Dr. Qi Han
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • magnetic anomaly sensing
  • magnetic sensor technology
  • magnetic detection platform
  • magnetic data processing

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Published Papers (3 papers)

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Research

19 pages, 11011 KiB  
Article
A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging
by Chien-Ching Chiu, Che-Yu Lin, Yu-Jen Chi, Hsiu-Hui Hsu, Po-Hsiang Chen and Hao Jiang
Sensors 2024, 24(15), 4994; https://doi.org/10.3390/s24154994 - 1 Aug 2024
Cited by 1 | Viewed by 843
Abstract
In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to [...] Read more.
In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions. Full article
(This article belongs to the Special Issue Advances in Magnetic Anomaly Sensing Systems)
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14 pages, 4886 KiB  
Article
Structural Design and Parameter Optimization of Magnetic Gradient Tensor Measurement System
by Gaigai Liu, Yingzi Zhang and Wenyi Liu
Sensors 2024, 24(13), 4083; https://doi.org/10.3390/s24134083 - 24 Jun 2024
Cited by 1 | Viewed by 935
Abstract
Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric [...] Read more.
Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10−10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers. Full article
(This article belongs to the Special Issue Advances in Magnetic Anomaly Sensing Systems)
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15 pages, 5391 KiB  
Article
Determinants of Maximum Magnetic Anomaly Detection Distance
by Hangcheng Li, Jiaming Luo, Jiajun Zhang, Jing Li, Yi Zhang, Wenwei Zhang and Mingji Zhang
Sensors 2024, 24(12), 4028; https://doi.org/10.3390/s24124028 - 20 Jun 2024
Viewed by 872
Abstract
The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants [...] Read more.
The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are established, calibrated and analyzed according to the experiment configuration. A maximum detectable distance map as a function of target size and measurement resolution is then obtained from parametric sweeping on an experimentally calibrated finite element analysis model. We find that the logarithm of detectable distance is positively proportional to the logarithm of object size while negatively proportional to the logarithm of resolution, within the ranges of 1 m~500 m and 1 pT~1 μT, respectively. A three-parameter empirical formula (namely distance-size-resolution logarithmic relationship) is firstly developed to determine the most economic sensor configuration for a given detection task, to estimate the maximum detection distance for a given magnetic sensor and object, or to evaluate minimum detectable object size at a given magnetic anomaly detection scenario. Full article
(This article belongs to the Special Issue Advances in Magnetic Anomaly Sensing Systems)
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