Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization
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...
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/15/2349 |
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| author | Lei Huang Zhihui Chen Jun Guan Jian Huang Wenjun Yi |
| author_facet | Lei Huang Zhihui Chen Jun Guan Jian Huang Wenjun Yi |
| author_sort | Lei Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-474fb6435d854f65a4af033bba8bdcca |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-474fb6435d854f65a4af033bba8bdcca2025-08-20T03:36:41ZengMDPI AGMathematics2227-73902025-07-011315234910.3390/math13152349Offline Magnetometer Calibration Using Enhanced Particle Swarm OptimizationLei Huang0Zhihui Chen1Jun Guan2Jian Huang3Wenjun Yi4National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, ChinaNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, ChinaTo 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.https://www.mdpi.com/2227-7390/13/15/2349error calibrationmagnetometerPSOellipsoid fitting |
| spellingShingle | Lei Huang Zhihui Chen Jun Guan Jian Huang Wenjun Yi Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization Mathematics error calibration magnetometer PSO ellipsoid fitting |
| title | Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization |
| title_full | Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization |
| title_fullStr | Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization |
| title_full_unstemmed | Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization |
| title_short | Offline Magnetometer Calibration Using Enhanced Particle Swarm Optimization |
| title_sort | offline magnetometer calibration using enhanced particle swarm optimization |
| topic | error calibration magnetometer PSO ellipsoid fitting |
| url | https://www.mdpi.com/2227-7390/13/15/2349 |
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