Magnetic Anomaly Detection Using Full Magnetic Gradient Orthonormal Basis Function

Qin, Yijie; Li, Keyan; Yao, Chen; Wang, Xianran; Ouyang, Jun; Yang, Xiaofei

doi:10.1109/jsen.2020.3003680

Cited by 28 publications

(6 citation statements)

References 29 publications

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“…According to ( 19)- (21), variables a 1 -a 3 constitute a set of linearly independent functions due to the fact that the they are not equal to zero in the domain of v [19]. Therefore, these three functions can be transformed to orthonormal bases by the Gram-Schmidt procedure as follows:…”

Section: Aead Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Active Electric Anomaly Detection Method for Underwater Targets Based on the Orthonormal Basis Function

Zhao

Shang

et al. 2022

JMSE

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Electric anomaly detection (EAD) has been widely used for target detection in underwater areas. However, due to the high path loss in the water, an electric anomaly is usually submerged in environmental noise and interference, which affects the detection performance of traditional anomaly detection methods. To address this problem and improve the detection accuracy in a low signal-to-noise ratio (SNR) environment, this paper proposes an active electric anomaly detection (AEAD) method based on the orthonormal basis function (OBF). First, a four-electrode active detection system was designed. Then, a set of OBFs based on the electric field disturbance model were derived to describe the detection system characteristic, linearly and effectively. Finally, an AEAD system was designed, and the proposed method was verified experimentally using a electromagnetic simulation tool to detect a spherical anomaly target. The experimental results show that, compared with the traditional AEAD methods, the proposed method has a better performance.

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Section: Aead Methodsmentioning

confidence: 99%

“…In the signal processing procedure ( 18) and (31), the value of the CPA distance R 0 is commonly unknown. In Qin's work, R 0 can be estimated by means of a multi-channel manner for several presumed values [19]. In this study, It is considered that a target is lying on the surface or subsurface of the river bed.…”

Section: Active Electric Field At Targetmentioning

confidence: 99%

Active Electric Anomaly Detection Method for Underwater Targets Based on the Orthonormal Basis Function

Zhao

Shang

et al. 2022

JMSE

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“…Figure 2 is a schematic diagram of the experiment. Do low-speed reciprocating motion in a test pool of 100m*60m*8m, set up test points on the shore, and place two three-axis high-sensitivity magnetic sensors [7] in front of the test points, with a sampling frequency of 1000 SPS. The distance to the test point of the underwater target is 4m, and the measurement records that the distance of the underwater target is 1m to the test point, and the power frequency magnetic field amplitude change of 1.5m under the suspended water surface.…”

Section: Test Modelmentioning

confidence: 99%

Simulation and measurement of power-frequency magnetic anomalies of underwater target

Tian²,

Li³

et al. 2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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Aiming at the problem that traditional underwater detection technology has been unable to meet the detection requirements, a new type of power-frequency (50Hz) magnetic field detection method is proposed. The space power frequency magnetic field model of the ferromagnetic cylindrical underwater target is deduced, and the magnetic anomaly of the ferromagnetic cylindrical underwater target rotating 0°, 30°, and 90° along the axis direction under the background of the power frequency magnetic field is simulated. The field distribution is further analyzed for the three-component magnetic anomaly spatial distribution law of ferromagnetic underwater targets. Through experiments on the scaled model, the magnetic induction intensity of the scaled model is measured, and the correctness and consistency of the spatial distribution of the power frequency magnetic anomalies of the ferromagnetic cylinder are verified, which is provided thinking for the design of power frequency electromagnetic detection of underwater targets.

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“…Simultaneously, some scholars have harnessed deep learning and neural networks to craft magnetic anomaly models, optimizing network efficiency by fine tuning the learning rate to improve the signal-to-noise ratio of target features. Constructed on the basis of ideal uniform motion, these models, when meshed with enhanced networks and OBF detectors [13][14][15][16], manifest a marked reduction in noise interference, fostering adept magnetic anomaly detection. In essence, magnetic anomaly detection strategies must duly factor in the attributes of the target, including the motion state, velocity, and other characteristics.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Orthogonal Basis Function Detection Method for Unknown Magnetic Target Motion State

Wang,

Zheng,

Liu

et al. 2024

Applied Sciences

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Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in practical detection. And, the non-ideal uniform motion of the magnetic target further complicates the process and adversely impacts the detector’s performance. To address this challenge, this paper introduces an adaptive scale factor method based on orthogonal basis function decomposition. This new method can be used to adjust the relative velocity between the detection platform and the magnetic target and to refine the characteristic time in the basis functions. In this paper, a mathematical relationship between the scale factor and the relative velocity is established, which is subsequently fitted into a Gauss function curve. The optimal scale factor, denoted as β, is adaptively chosen from the fitting curve when the magnetic target moves at a non-ideal uniform velocity with an unknown motion state. The results of simulations indicate that the scale factor improves the signal-to-noise ratio of the magnetic anomaly signals in a non-ideal state. And, this method can improve the energy value of OBF decomposition by 17.7%. Simultaneously, this method ensures that the moment the magnetic target passes the CPA coincides with the energy peak of the orthogonal basis detection, which improves the accuracy by 54.1% compared with the traditional method. The effectiveness and precision of the proposed method are verified using simulations and practical experiments.

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Magnetic Anomaly Detection Using Full Magnetic Gradient Orthonormal Basis Function

Cited by 28 publications

References 29 publications

Active Electric Anomaly Detection Method for Underwater Targets Based on the Orthonormal Basis Function

Active Electric Anomaly Detection Method for Underwater Targets Based on the Orthonormal Basis Function

Simulation and measurement of power-frequency magnetic anomalies of underwater target

Adaptive Orthogonal Basis Function Detection Method for Unknown Magnetic Target Motion State

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