On uniqueness of solution of a reverse problem of magnetic location

Kasatkin, S. I.; Polyakov, O. P.; Rusakova, N. E.; Rusakov, A.E.

doi:10.1016/j.jmmm.2006.01.027

Cited by 10 publications

(2 citation statements)

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“…Localization experiments were conducted, where the maximum error is about 7 mm when the source-sensor distance is from 80 to 140 mm. Kasatkin et al [ 14 ] studied the solution uniqueness analysis of the magnetic dipole location problem based on the two-point known magnetic field intensity vector value. It was shown that the usage of two triples (pairs) of sensors is sufficient to solve a 3D (2D) problem of arbitrary magnetic dipole localization with a satisfactory number of crude errors.…”

Section: Introductionmentioning

confidence: 99%

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure

Han

Zhang

et al. 2022

Sensors

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After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east–west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method’s recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification.

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Section: Introductionmentioning

confidence: 99%

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure

Han

Zhang

et al. 2022

Sensors

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“…With the continuous development of magnetic sensor technology and the deepening of the ship's magnetic field study. The use of magnetic fields to locate or identify ships and to detonate weapons gradually become a hot research [1][2][3][4]. Dynamic magnetic detection technology is a new detection method that is developed by navies recently.…”

Section: Introductionmentioning

confidence: 99%

Research on Magnetometer Positioning Technology Based on a Variable Magnetic Source

Shu

Liu

Zhang

et al. 2014

AMM

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The precision of magnetometer positioning is affected by the geomagnetic anomaly, and traditional positioning technology cannot be applied to the motion sensor positioning. A magnetometer localization method based on a variable magnetic source is proposed. The method localizes a magnetic sensor by the absolute value of the magnetic field. Positioning process does not use geomagnetic coordinates to avoid the effects of geomagnetic anomalies. The method uses time-division multiplexing technology to achieve the motion sensor positioning. Simulation results show that the average positioning error is 0.3% and the maximum positioning error is 1.3% when the tilt angle of the magnetic source is ±1 degree. It has the advantages of high precision.

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Research on magnetic detection target recognition method based on residual network combined with magnetic moment estimation

Wen,

Han,

Gao

et al. 2024

Measurement

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On uniqueness of solution of a reverse problem of magnetic location

Cited by 10 publications

References 0 publications

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure

Research on Magnetometer Positioning Technology Based on a Variable Magnetic Source

Research on magnetic detection target recognition method based on residual network combined with magnetic moment estimation

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