This conference paper is devoted to an experimen- tal assessment of a bi-polar near-field far-field (NFFF) transforma- tion with probe compensation, which is particularly convenient from the data reduction standpoint when characterising a flat an- tenna under test (AUT). The proposed technique utilizes the non- redundant sampling representations of the electromagnetic fields for developing an efficient probe voltage sampling representation on the scan plane, which requires the knowledge of the NF bi-polar samples at a reduced number of sampling points. Then, these sam- ples are suitably interpolated by a two-dimensional optimal sam- pling interpolation expansion to accurately reconstruct the plane- rectangular NF data needed by the standard Leach&Paris’s NFFF transformation. To properly take into account the AUT ge- ometry, a disc having diameter equal to the AUT largest dimen- sion is adopted as modeling surface. Such a surface allows one a more effective AUT modeling from the NF data reduction stand- point than the other proposed modeling surfaces for quasi-planar AUTs (the oblate spheroid or the two-bowls), because it has the capability to reduce very significantly the related volumetric re- dundance, fitting very well the AUT geometry. Experimental re- sults are shown to assess the efficacy of this NFFF transformation.

An Efficient bi-polar near-field far-field transformation for flat AUTs

Francesco D’Agostino;Flaminio Ferrara;Claudio Gennarelli;Rocco Guerriero;Massimo Migliozzi
2022

Abstract

This conference paper is devoted to an experimen- tal assessment of a bi-polar near-field far-field (NFFF) transforma- tion with probe compensation, which is particularly convenient from the data reduction standpoint when characterising a flat an- tenna under test (AUT). The proposed technique utilizes the non- redundant sampling representations of the electromagnetic fields for developing an efficient probe voltage sampling representation on the scan plane, which requires the knowledge of the NF bi-polar samples at a reduced number of sampling points. Then, these sam- ples are suitably interpolated by a two-dimensional optimal sam- pling interpolation expansion to accurately reconstruct the plane- rectangular NF data needed by the standard Leach&Paris’s NFFF transformation. To properly take into account the AUT ge- ometry, a disc having diameter equal to the AUT largest dimen- sion is adopted as modeling surface. Such a surface allows one a more effective AUT modeling from the NF data reduction stand- point than the other proposed modeling surfaces for quasi-planar AUTs (the oblate spheroid or the two-bowls), because it has the capability to reduce very significantly the related volumetric re- dundance, fitting very well the AUT geometry. Experimental re- sults are shown to assess the efficacy of this NFFF transformation.
978-1-6654-8362-9
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4802515
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