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2011-12-12

Calibration of a Six-Port Position Sensor via Support Vector Regression

By Hao Peng, Tao Yang, and Ziqiang Yang
Progress In Electromagnetics Research C, Vol. 26, 71-81, 2012
doi:10.2528/PIERC11101707

Abstract

In this paper, a calibration technique for the position sensor via support vector regression (SVR) is proposed. The position sensor adopts a zero-intermediate frequency architecture based on a six-port network, which is used for directly measuring the phase differences and indirectly reflecting the position. The SVR, which implements the structural risk minimization (SRM) principle, provides a good generalization ability from size-limited data sets. The results indicate that the SVR model can achieve a great predictive ability in positioning, with an accuracy of 2.41 mm over a distance range of 274.5 mm.

Citation


Hao Peng, Tao Yang, and Ziqiang Yang, "Calibration of a Six-Port Position Sensor via Support Vector Regression," Progress In Electromagnetics Research C, Vol. 26, 71-81, 2012.
doi:10.2528/PIERC11101707
http://jpier.org/PIERC/pier.php?paper=11101707

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