Interpretation of signature waveform characteristics for magnetic anomaly detection using tunneling magnetoresistive sensor

Shen, Ying; Wang, Jiazeng; Shi, Jiedong; Zhao, Shufan; Gao, Junqi

doi:10.1016/j.jmmm.2019.04.016

Cited by 15 publications

(3 citation statements)

References 22 publications

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“…Some experts embarked on experimental ventures with variably paced magnetic targets, capturing magnetic anomaly readings using ultra-sensitive sensors. Notably, when privy to the relative velocity data, they corroborated the assertion that sensor coverage angles remain unaffected by velocity fluctuations and the closest point of approach (CPA) [11,12]. 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.…”

Section: Introductionmentioning

confidence: 75%

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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Section: Introductionmentioning

confidence: 75%

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

Wang,

Zheng,

Liu

et al. 2024

Applied Sciences

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“…With the continuous development of magnetic sensors towards miniaturization, lightweight, and array, TMR, one of the most successful magnetic sensors in industrial applications in recent years, plays an increasingly important role in the magnetic sensor market [4]. Due to the small size of the sensing unit, high integration, flexible design, and other characteristics, TMR sensors are widely used in current detection [5,6], magnetic anomaly detection (MAD) [7][8][9], angular velocity meter [10][11][12], civil meter [13,14], eddy current nondestructive testing (NDT) [15][16][17][18][19][20], bio-magnetic signal measurement [21,22], and other aspects. MAD has been paid more attention to because of its large detection range, good penetrability, and high detection sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Portable Magnetic Detectors Based on TMR Sensors

Zhu

et al. 2023

J. Phys.: Conf. Ser.

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The tunnel magnetoresistance sensor (TMR) is the fourth generation of the industrial magnetic sensor, which has the characteristics of low power consumption, high sensitivity, and low- temperature dependence. The portable magnetic detector based on the TMR sensor can effectively pick up the magnetic signal by gradient probes based on TMR sensors. After the magnetic signal is amplified, filtered, and normalized, the magnetic source orientation and amplitude are displayed on the LCD screen. The experiment proves that the portable magnetic detector can effectively detect a magnetic field larger than 100 NT at a distance of 30 cm, and the design meets the experimental requirement. It is of great practical significance to detect the magnetic source in an unshielded indoor environment.

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“…Recently, researchers have proposed several methods of magnetic anomaly detection [10][11][12][13][14][15][16][17][18][19]. These methods can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature

et al. 2020

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Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise. In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature. The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD. According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively. Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise. Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious. The magnetic anomaly is detected whenever the entropy feature drops below the threshold. Thus, it is effective for revealing the weak magnetic anomaly by the proposed method. The measured magnetic noise is used to validate the performance of the proposed method. The results show that the detection probability of the proposed method is higher with low input SNR.

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Interpretation of signature waveform characteristics for magnetic anomaly detection using tunneling magnetoresistive sensor

Cited by 15 publications

References 22 publications

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

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

Portable Magnetic Detectors Based on TMR Sensors

Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature

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