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Localization of charged objects using a planar electrostatic sensor array

Hu, Yonghui, Wang, Junkai, Li, Yi, Yan, Yong (2023) Localization of charged objects using a planar electrostatic sensor array. IEEE Transactions on Instrumentation and Measurement, . ISSN 0018-9456. E-ISSN 1557-9662. (doi:10.1109/tim.2023.3328090) (KAR id:103291)

Abstract

Localization of charged objects is of great interest in a diverse range of industrial, commercial and military applications. This paper presents a method for locating charged objects in a plane using an electrostatic sensor array. Based on a simplified analytical model of the electrostatic sensor embedded in a grounded surface, a closed-form solution to the localization problem is derived for a linear sensor array, while the Newton-Raphson numerical method is used to approximate the solution for three randomly positioned sensors. Numerical simulations show that the charged object can be accurately located using the linear sensor array in areas far from the electrodes and the grounded surface. Depending on the initial value, the numerical solution for randomly positioned sensors may converge to correct or incorrect results and may diverge as well.

Item Type: Article
DOI/Identification number: 10.1109/tim.2023.3328090
Additional information: © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled keywords: Localization; charged object; electrostatic sensor; point charge; analytical model; numerical solution
Subjects: T Technology
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: National Natural Science Foundation of China (https://ror.org/01h0zpd94)
Depositing User: Yong Yan
Date Deposited: 14 Oct 2023 08:49 UTC
Last Modified: 05 Nov 2024 13:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/103291 (The current URI for this page, for reference purposes)

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