Accurate Magnetic Object Localization Using Artificial Neural Network

Chen, Shengzhi; Zhu, Muhua; Zhang, Qing; Cai, Xuesong; Xiao, Bo

doi:10.1109/msn48538.2019.00019

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The position and orientation of the permanent magnets are generally estimated by building an optimization model of the target parameters based on a dipole model, measuring the magnetic field using an array of magnetic sensors, and using an optimization algorithm to minimize the cost function [10]. In addition, studies have been conducted on target position inversion tracking using recursive Bayesian estimation [11] or artificial neural networks [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources

Miao,

Zhang,

Zuo

et al. 2024

Sensors

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A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms.

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