Next Article in Journal
A Micromachined Piezoresistive Pressure Sensor with a Shield Layer
Next Article in Special Issue
On Event-Triggered Adaptive Architectures for Decentralized and Distributed Control of Large-Scale Modular Systems
Previous Article in Journal
Enhanced Positioning Algorithm of ARPS for Improving Accuracy and Expanding Service Coverage
Previous Article in Special Issue
Mathematical Model and Calibration Experiment of a Large Measurement Range Flexible Joints 6-UPUR Six-Axis Force Sensor
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1280; doi:10.3390/s16081280

High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis

1
Engineering Product Development (EPD) Pillar, Singapore University of Technology & Design (SUTD), Singapore 487372, Singapore
2
International Design Centre (IDC), Singapore University of Technology & Design (SUTD), Singapore 487372, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Dan Zhang
Received: 30 June 2016 / Revised: 3 August 2016 / Accepted: 9 August 2016 / Published: 12 August 2016
(This article belongs to the Special Issue Advanced Robotics and Mechatronics Devices)
View Full-Text   |   Download PDF [5717 KB, uploaded 12 August 2016]   |  

Abstract

In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison. View Full-Text
Keywords: artificial neural networks; magnetic sensors; principal component analysis; signal mapping; linear actuators artificial neural networks; magnetic sensors; principal component analysis; signal mapping; linear actuators
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Foong, S.; Sun, Z. High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis. Sensors 2016, 16, 1280.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top