Research on Temperature Error Calibration Method of Fluxgate Sensor

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. Compared with other temperature calibration methods, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE = 11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background.

Authors

Institutions

Publication Details

Journal
Journal of Advanced Computational Intelligence and Intelligent Informatics
Published
2025-05-19
DOI
https://doi.org/10.20965/jaciii.2025.p0583
Primary Topic
Magnetic Field Sensors Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Temperature Error Calibration Method of Fluxgate Sensor

Wang Luo, Xiangyun Hu, Daisuke Chugo, Haobin Dong et al.
Journal of Advanced Computational Intelligence and Intelligent Informatics
Magnetic Field Sensors Techniques
article

Research on Temperature Error Calibration Method of Fluxgate Sensor

Wang Luo, Xiangyun Hu, Daisuke Chugo, Haobin Dong, Jian Ge, Ruiping Yang, Jinhua She
article en

Abstract

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. Compared with other temperature calibration methods, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE = 11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background.

Journal of Advanced Computational Intelligence and Intelligent InformaticsVol. 29(3)
Tokyo University of Technology (JP), Kwansei Gakuin University (JP), China University of Geosciences (CN), Shandong Institute of Automation (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Openalex Percentile: Top 91%
Magnetic Field Sensors Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.