Magnetic Tensor Sensor for Gradient-Based Localization of Ferrous Object in Geomagnetic Field

被引:42
|
作者
Lee, Kok-Meng [1 ,2 ]
Li, Min [1 ]
机构
[1] Georgia Inst Technol, Woodruff Sch Mech Engn, Atlanta, GA 30332 USA
[2] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
基金
美国国家科学基金会;
关键词
Ferrous object; geomagnetic field; identification and localization; magnetic object; magnetic tensor; magnetics;
D O I
10.1109/TMAG.2016.2535307
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a gradient-based method, along with the design concept, characteristics, and operating range of a magnetic tensor sensor (MTS), for locating and identifying a ferrous/magnetic object in the presence of geomagnetic field. This method characterizes the magnetic moment M and position vector R of a ferrous/magnetic object in terms of two scalar parameters (an orientation-insensitive P and a distance-insensitive.) derived from the measured magnetic tensor data. These scalar parameters offer an excellent alternative to the traditional (M and R) in characterizing a magnetic object with an arbitrary shape for some applications when the dipole model is a poor approximation. With a prototype MTS that has been developed and experimentally validated, the effectiveness and accuracy of the gradient-based method are demonstrated with two different types of compact objects. The first object is a uniformly magnetized cylindrical permanent magnet, commonly used as an engineered landmark for machine applications, where the interest is to accurately determine M and/or R. The second object is an example of a general ferrous object with a non-uniform shape to illustrate the detection and approximate localization of a ferrous object for applications such as visually impaired assistance.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] A closed-form formula for magnetic dipole localization by measurement of its magnetic field vector and magnetic gradient tensor
    Yin, Gang
    Zhang, Lin
    Jiang, Hong
    Wei, Zhi
    Xie, Yan
    JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, 2020, 499
  • [42] Analysis of gradient-based routing protocols in sensor networks
    Faruque, J
    Psounis, K
    Helmy, A
    DISTRIBUTED COMPUTING IN SENSOR SYSTEMS, PROCEEDINGS, 2005, 3560 : 258 - 275
  • [43] GRADIENT-BASED MICRO SENSOR ROUTING PROTOCOL IN WIRELESS SENSOR NETWORKS
    Gao, Deyun
    Liang, Lulu
    Du, Peng
    Zhang, Hongke
    2009 IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT, PROCEEDINGS, 2009, : 45 - 49
  • [44] Anisotropic gradient-based filtering for object segmentation in medical images
    Joao, Ana
    Gambaruto, Alberto
    Sequeira, Adelia
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION, 2020, 8 (06): : 621 - 630
  • [45] Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors
    Riedlinger, Tobias
    Rottmann, Matthias
    Schubert, Marius
    Gottschalk, Hanno
    2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, : 3910 - 3920
  • [46] The magnetic field and magnetic gradient tensor for a right circular cylinder
    McKenzie, K. Blair
    EXPLORATION GEOPHYSICS, 2022, 53 (03) : 329 - 358
  • [47] Object Localization by Construction of an Asymmetric Isobody of the Magnetic Gradient Tensor Contraction Using Two Identical Permanent Magnets
    Martinovic, Dean
    Vuletic, Jelena
    Stuhne, Dario
    Orsag, Matko
    Kovacic, Zdenko
    IEEE TRANSACTIONS ON MAGNETICS, 2023, 59 (04)
  • [48] Gradient-based optimization for regression in the functional tensor-train format
    Gorodetsky, Alex A.
    Jakeman, John D.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2018, 374 : 1219 - 1238
  • [49] Underwater Continuous Localization Based on Magnetic Dipole Target Using Magnetic Gradient Tensor and Draft Depth
    Huang, Yu
    Wu, Li-Hua
    Sun, Feng
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (01) : 178 - 180
  • [50] Gradient-Based Instance-Specific Visual Explanations for Object Specification and Object Discrimination
    Zhao, Chenyang
    Hsiao, Janet H.
    Chan, Antoni B.
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2024, 46 (09) : 5967 - 5985