Investigation and Modeling of Large Barkhausen Jumps Dynamics in Low-Power Fluxgate Magnetometers

Weiss, Eyal; Alimi, Roger; Ivry, Amir; Fisher, Elad

doi:10.1109/jsen.2018.2885779

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…According to the RTD detection theory [12], [13], [25], [26], the positive threshold Vtp and negative threshold Vtn of the hysteresis comparator should be symmetric and fixed, Vtp=-Vtn=Vt. Because the noise of the RTD probe output is not uniformly distributed [20] - [23], so the detection resolution is threshold sensitive.…”

Section: A the Magneto-sensitive Resolution Of Rtd Methodsmentioning

confidence: 99%

Performance Degradation Effect Countermeasures in Residence Times Difference (RTD) Fluxgate Magnetic Sensors

Zhang

Shi

et al. 2019

IEEE Sensors J.

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This paper aims to explore the detection defect of residence times difference (RTD) fluxgate working in low-power mode. It presents the countermeasures for sensor resolution improvement and linearity enhancement. The main defects are amplitude and symmetry changes induced in the output spikes of fluxgate probe due to the magnetic field. These defects lead to thresholds deviation and asymmetry, then causes severe performance degradation especially on detection resolution and linearity according to the RTD theory. To overcome such effects, the optimized RTD method based on voltage extraction and feedback technology is proposed to implement magnetic field compensation and achieve a zero-field running regime of the RTD fluxgate. In this regard, the sensor linearity is improved by a factor of 38, and the resolution degradation effect is suppressed more than 6 times, verified by the laboratory experiments. The optimized detection method proposed in this paper demonstrated a great potential to achieve lower power consumption, will make the RTD fluxgate more promising technology among bio-magnetic applications.

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Section: A the Magneto-sensitive Resolution Of Rtd Methodsmentioning

confidence: 99%

Performance Degradation Effect Countermeasures in Residence Times Difference (RTD) Fluxgate Magnetic Sensors

Zhang

Shi

et al. 2019

IEEE Sensors J.

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“…The "stuck" domains disturb the effective permeability of the core, which is translated to a rapid change in fluxgate sensitivity. An experimentally physical model for the dc jumps based on an expansion of the Landau Lifshitz Gilbert (LLG) equation and an expansion to include the core excitation dynamics has been already presented [Weiss 2019]. Regardless of the physical origin of dc jumps, from a practical, applicable point of view [Alimi 2009], we believe it is crucial to be able not only to detect, but also to distinguish dc jumps from very similar signal patterns.…”

Section: Introductionmentioning

confidence: 99%

“…However, the conventional noise measurement does not always catch the dc jump because of its sparsity. Nevertheless, dc jumps persist in almost all the sensor cores although at a slower rate [Weiss 2019]. As a result, low-power consuming fluxgates cannot be utilized to their full potential because their output is afflicted with dc jumps that severely impede their performance.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Detection Algorithms for Large Barkhausen Jumps in Cluttered Environment

et al. 2019

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Modern magnetic sensor arrays conventionally utilize state of the art low power magnetometers such as parallel and orthogonal fluxgates. Low power fluxgates tend to have large Barkhausen jumps that appear as a dc jump in the fluxgate output. This phenomenon deteriorates the signal fidelity and effectively increases the internal sensor noise. Even if sensors that are more prone to dc jumps can be screened during production, the conventional noise measurement does not always catch the dc jump because of its sparsity. Moreover, dc jumps persist in almost all the sensor cores although at a slower but still intolerable rate. Even if dc jumps can be easily detected in a shielded environment, when deployed in presence of natural noise and clutter, it can be hard to positively detect them. This work fills this gap and presents algorithms that distinguish dc jumps embedded in natural magnetic field data. To improve robustness to noise, we developed two machine learning algorithms that employ temporal and statistical physical-based features of a pre-acquired and well-known experimental data set. The first algorithm employs a support vector machine classifier, while the second is based on a neural network architecture. We compare these new approaches to a more classical kernel-based method. To that purpose, the receiver operating characteristic curve is generated, which allows diagnosis ability of the different classifiers by comparing their performances across various operation points. The accuracy of the machine learning-based algorithms over the classic method is highly emphasized. In addition, high generalization and robustness of the neural network can be concluded, based on the rapid convergence of the corresponding receiver operating characteristic curves.

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Optimization of the Temperature Stability of Fluxgate Sensors for Space Applications

et al. 2021

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Investigation and Modeling of Large Barkhausen Jumps Dynamics in Low-Power Fluxgate Magnetometers

Cited by 4 publications

References 30 publications

Performance Degradation Effect Countermeasures in Residence Times Difference (RTD) Fluxgate Magnetic Sensors

Performance Degradation Effect Countermeasures in Residence Times Difference (RTD) Fluxgate Magnetic Sensors

Machine-Learning Detection Algorithms for Large Barkhausen Jumps in Cluttered Environment

Optimization of the Temperature Stability of Fluxgate Sensors for Space Applications

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