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

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 8; p. 1280
• Main Authors: Foong, Shaohui, Sun, Zhenglong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12-08-2016; MDPI
• Subjects: artificial neural networks; Computer simulation; Control systems; Feedback control; linear actuators; Linear filters; Localization; Magnetic fields; magnetic sensors; Mapping; Neural networks; Position sensing; principal component analysis; Principal components analysis; Random noise; Sensors; signal mapping; Translational motion; artificial neural networks; magnetic sensors; linear actuators; principal component analysis; signal mapping
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16081280

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.