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

Abstract

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.

Performance parameters prediction of slotted microstrip antennas with modified ground plane using support vector machine

“…Recently, the SVM concept has been introduced to predict the specific model accurately and instantly in several cases [16–31]. These cases are mentioned as: characterization of communication networks [16], annual runoff forecasting [17], blind multiuser detector for chaos-based CDMA system [18], building of synthesis of transmission line for microwave-integrated circuit [19], dielectric target detection through wall [20], urban impervious surface estimation from RADARSAT-2 Polari metric data [21], integrating a grid scheme (GS) into a least-squares support vector machine (LSSVM) with a mixed kernel to solve a data classification problem [22], estimating highly selective channels for Orthogonal Frequency Division Multiplexing system by complex LSSVM [23], calibration for position sensor [24], identify monitors on the basis of their unintended electromagnetic radiation [25], electromechanical coupling for microwave filter tuning [26], and detection and delineation of P- and T-wave in Electrocardiogram signals [27]. Consequently, different types of problems have been resolved using the formulation of SVMs but unfortunately, the literature of SVMs formulation in electromagnetic and microstrip antennas problems is very much limited [28–31].…”

Design and Implementation of a Practical Direction Finding Receiver

“…The following SVR parameters should first be obtained: the constant definition of kernel function (γ), tolerance of termination criterion (ε), penalty parameter (C), and constant ν. ν ∈ [0, 1] is the parameter that controls the number of support vectors. The K-fold Cross-Validation (K-CV) method [19], which was also applied in our previous work [13], is used to calculate the optimal parameters of γ and C. The SVR parameters are as follows: ε = 0.0001, ν = 0.1, C = 0.965936, and γ = 0.5. The Root Mean Square Error (RMSE) and Pearson ProductMoment correlation coefficient (R) are calculated to determine the accuracy of the SVR model.…”

“…In this paper, we propose a calibration technique based on support vector regression (SVR). Support vector machine (SVM) theory has generated fruitful results in data classification and regression [10][11][12][13]. SVM embodies the structural risk minimization (SRM) principle instead of the traditional empirical risk minimization (ERM) of ANNs.…”

One Decade Microwave Forward Network Analyzer Based on the Multi-Port Technique

“…Since the introduction of the sixport technique, there has been considerable work in the analysis and realization of six-port reflectometers [4][5][6][7][8]. The six-port concept takes also advantage of the capability of easily and accurately retrieving the magnitude and phase of a complex microwave signal in different kind of applications such as radar sensors [9][10][11][12] and telecommunications [13][14][15][16].…”