Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization

Lei Huang, Zhihui Chen, Jun Guan, Jian Huang and Wenjun Yi ()
Additional contact information
Lei Huang: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Zhihui Chen: School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Jun Guan: School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Jian Huang: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Wenjun Yi: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China

Mathematics, 2025, vol. 13, issue 15, 1-14

Abstract: To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications.

Keywords: error calibration; magnetometer; PSO; ellipsoid fitting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/15/2349/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/15/2349/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:15:p:2349-:d:1708075

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().