AI-Assisted Passive Magnetic Distance/Position Sensor
Magnetic sensing technology is crucial for non-contact distance and position measurement. Due to the nonlinear characteristics of the magnetic fields from permanent magnets, conventional magnetic sensors struggle with accurate distance and position determination. To address this, we propose a distan...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1132 |
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| author | Chaoyi Qiu Zhenghong Qian Qiao Qi Ruigang Wang Xiumei Li Ru Bai |
| author_facet | Chaoyi Qiu Zhenghong Qian Qiao Qi Ruigang Wang Xiumei Li Ru Bai |
| author_sort | Chaoyi Qiu |
| collection | DOAJ |
| description | Magnetic sensing technology is crucial for non-contact distance and position measurement. Due to the nonlinear characteristics of the magnetic fields from permanent magnets, conventional magnetic sensors struggle with accurate distance and position determination. To address this, we propose a distance/position sensor that employs a customized back propagation (BP) neural network. By detecting the magnetic field variations induced by a permanent magnet, the proposed sensor can effectively model the nonlinear mapping between magnetic field strength and distance, thereby enabling precise distance and position measurement. Experimental results demonstrate that the BP neural network approach, when employing a single magnetic sensor, exhibits a measurement error in the range of −0.0268 mm to 0.0362 mm over a distance of 0–70 mm, which is significantly lower than traditional methods based on the magnetic dipole model and the Levenberg–Marquardt (LM) algorithm. Increasing the number of sensors to three reduces the error further to −0.0107 mm to 0.0093 mm. Furthermore, when employing four magnetic sensors for position measurement within a 60 mm × 60 mm planar area, the positioning errors along the <i>x</i>-axis and <i>y</i>-axis are confined to the ranges of −0.6168 mm to 1.1312 mm and −0.6001 mm to 0.5133 mm, respectively. |
| format | Article |
| id | doaj-art-04272678b7fa47d981805abd090c9f61 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-04272678b7fa47d981805abd090c9f612025-08-20T03:12:15ZengMDPI AGSensors1424-82202025-02-01254113210.3390/s25041132AI-Assisted Passive Magnetic Distance/Position SensorChaoyi Qiu0Zhenghong Qian1Qiao Qi2Ruigang Wang3Xiumei Li4Ru Bai5School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaMagnetic sensing technology is crucial for non-contact distance and position measurement. Due to the nonlinear characteristics of the magnetic fields from permanent magnets, conventional magnetic sensors struggle with accurate distance and position determination. To address this, we propose a distance/position sensor that employs a customized back propagation (BP) neural network. By detecting the magnetic field variations induced by a permanent magnet, the proposed sensor can effectively model the nonlinear mapping between magnetic field strength and distance, thereby enabling precise distance and position measurement. Experimental results demonstrate that the BP neural network approach, when employing a single magnetic sensor, exhibits a measurement error in the range of −0.0268 mm to 0.0362 mm over a distance of 0–70 mm, which is significantly lower than traditional methods based on the magnetic dipole model and the Levenberg–Marquardt (LM) algorithm. Increasing the number of sensors to three reduces the error further to −0.0107 mm to 0.0093 mm. Furthermore, when employing four magnetic sensors for position measurement within a 60 mm × 60 mm planar area, the positioning errors along the <i>x</i>-axis and <i>y</i>-axis are confined to the ranges of −0.6168 mm to 1.1312 mm and −0.6001 mm to 0.5133 mm, respectively.https://www.mdpi.com/1424-8220/25/4/1132magnetic sensingnonlinear magnetic fielddistance/position sensorBP neural network |
| spellingShingle | Chaoyi Qiu Zhenghong Qian Qiao Qi Ruigang Wang Xiumei Li Ru Bai AI-Assisted Passive Magnetic Distance/Position Sensor Sensors magnetic sensing nonlinear magnetic field distance/position sensor BP neural network |
| title | AI-Assisted Passive Magnetic Distance/Position Sensor |
| title_full | AI-Assisted Passive Magnetic Distance/Position Sensor |
| title_fullStr | AI-Assisted Passive Magnetic Distance/Position Sensor |
| title_full_unstemmed | AI-Assisted Passive Magnetic Distance/Position Sensor |
| title_short | AI-Assisted Passive Magnetic Distance/Position Sensor |
| title_sort | ai assisted passive magnetic distance position sensor |
| topic | magnetic sensing nonlinear magnetic field distance/position sensor BP neural network |
| url | https://www.mdpi.com/1424-8220/25/4/1132 |
| work_keys_str_mv | AT chaoyiqiu aiassistedpassivemagneticdistancepositionsensor AT zhenghongqian aiassistedpassivemagneticdistancepositionsensor AT qiaoqi aiassistedpassivemagneticdistancepositionsensor AT ruigangwang aiassistedpassivemagneticdistancepositionsensor AT xiumeili aiassistedpassivemagneticdistancepositionsensor AT rubai aiassistedpassivemagneticdistancepositionsensor |