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一种基于交变磁场的运动物体定位新方法。

A Novel Method of Localization for Moving Objects with an Alternating Magnetic Field.

作者信息

Gao Xiang, Yan Shenggang, Li Bin

机构信息

School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.

Key Laboratory of Ocean Acoustics and Sensing, Ministry of Industry and Information Technology, Xi'an 710072, China.

出版信息

Sensors (Basel). 2017 Apr 21;17(4):923. doi: 10.3390/s17040923.

DOI:10.3390/s17040923
PMID:28430153
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5426919/
Abstract

Magnetic detection technology has wide applications in the fields of geological exploration, biomedical treatment, wreck removal and localization of unexploded ordinance. A large number of methods have been developed to locate targets with static magnetic fields, however, the relation between the problem of localization of moving objectives with alternating magnetic fields and the localization with a static magnetic field is rarely studied. A novel method of target localization based on coherent demodulation was proposed in this paper. The problem of localization of moving objects with an alternating magnetic field was transformed into the localization with a static magnetic field. The Levenberg-Marquardt (L-M) algorithm was applied to calculate the position of the target with magnetic field data measured by a single three-component magnetic sensor. Theoretical simulation and experimental results demonstrate the effectiveness of the proposed method.

摘要

磁探测技术在地质勘探、生物医学治疗、沉船打捞以及未爆弹药定位等领域有着广泛应用。已经开发出大量利用静磁场来定位目标的方法,然而,交变磁场下运动目标的定位问题与静磁场定位之间的关系却鲜有研究。本文提出了一种基于相干解调的目标定位新方法。将交变磁场下运动目标的定位问题转化为静磁场定位问题。利用Levenberg-Marquardt(L-M)算法,根据单个三分量磁传感器测量的磁场数据来计算目标位置。理论仿真和实验结果验证了该方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/0ecd570f057a/sensors-17-00923-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/d2d81a1f0e19/sensors-17-00923-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/5688a92b4e49/sensors-17-00923-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/511fa1442e3b/sensors-17-00923-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/35ef2480e290/sensors-17-00923-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/4d5c52ae4a9f/sensors-17-00923-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/4ee0d7bc317d/sensors-17-00923-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/9ab9d9ae094f/sensors-17-00923-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/49ac31a975ce/sensors-17-00923-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/bbc7259a917e/sensors-17-00923-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/1fc49d583faa/sensors-17-00923-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/9f155614bfa4/sensors-17-00923-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/3e0126803dff/sensors-17-00923-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/b1678a51ea49/sensors-17-00923-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/0ecd570f057a/sensors-17-00923-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/d2d81a1f0e19/sensors-17-00923-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/5688a92b4e49/sensors-17-00923-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/511fa1442e3b/sensors-17-00923-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/35ef2480e290/sensors-17-00923-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/4d5c52ae4a9f/sensors-17-00923-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/4ee0d7bc317d/sensors-17-00923-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/9ab9d9ae094f/sensors-17-00923-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/49ac31a975ce/sensors-17-00923-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/bbc7259a917e/sensors-17-00923-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/1fc49d583faa/sensors-17-00923-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/9f155614bfa4/sensors-17-00923-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/3e0126803dff/sensors-17-00923-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/b1678a51ea49/sensors-17-00923-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4908/5426919/0ecd570f057a/sensors-17-00923-g014.jpg

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本文引用的文献

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A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects.一种用于移动磁性物体定位的专用遗传算法。
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考虑角旋转运动中利用三个磁传感器对铁磁目标进行定位
Sensors (Basel). 2017 Sep 11;17(9):2079. doi: 10.3390/s17092079.