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Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten. A Reduced Sampling Scheme for Planar Near-field Measurements Using Pointwise Probe Correction in the Spherical Harmonics Basis
| Institute of Electrical and Electronics Engineers -IEEE-: PhotonIcs & Electromagnetics Research Symposium - Spring, PIERS-Spring 2019. Proceedings : 17-20 June 2019, Rome, Italy Piscataway, NJ: IEEE, 2019 ISBN: 978-1-7281-3404-8 ISBN: 978-1-7281-3403-1 S.809-816 |
| PhotonIcs & Electromagnetics Research Symposium (PIERS-Spring) <41, 2019, Rome> |
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| Englisch |
| Konferenzbeitrag |
| Fraunhofer FHR () |
Abstract
Planar Near-Field (PNF) measurements are a reliable way to determine the radiation characteristics of an Antenna Under Test (AUT) when only a plane is of interest and far-field measurements are not an option. This plane is classically sampled in Cartesian coordinates on a grid following the Nyquist criterion, i.e., using sampling points with a distance of at least λ/2 in both axes. This sampling is normally kept independently of the distance between the plane and the AUT. Spherical Near-Field (SNF) measurements, on the other hand, are classically performed by sampling the surface of the minimum sphere enclosing the AUT following the Nyquist criterion. In this work, a reduced sampling grid for PNF measurements is introduced by using the radial projection of the sampling points on the minimum sphere of a classical SNF measurement onto a plane. Depending on the sampling, different solvers in the spherical harmonics basis are considered and the spherical mode coefficients are calculated. To describe the sampling points on a plane, pointwise probe correction is used, where different probe response constants are calculated for each measurement point. In this way, a different probe distance and probe orientation at each point are considered. Reconstruction results are compared to those of standard PNF sampling, showing that a reduction of measurement points by processing the acquired data in another basis is possible.