We propose a novel Near-Field-Far-Field (NF-FF) transformation technique for aperture antennas and cylindrical NF scanning. It exploits a proper aperture field representation, based on the use of the Prolate Spheroidal Wave Functions (PSWFs), accounting for the a priori information on shape and size of the Antenna Under Test (AUT). It furthermore allows a fast NF acquisition by employing a continuous and synchronized movement of probe and AUT, as well as on a helicoidal scan based on a nonredundant field representation. The NF-FF transformation problem is formulated as the inversion of a linear relation linking the unknown PSWF expansion coefficients to the NF samples and solved by a regularized Singular Value Decomposition (SVD) approach. Experimental results show the effectiveness of the approach, and how it enables a serious reduction of the measurement points without impairing the far-field estimation accuracy.
A SVD-based approach to helicoidal NF-FF transformations / Capozzoli, Amedeo; Curcio, Claudio; D'Elia, Giuseppe; F., Ferrara; C., Gennarelli; R., Guerriero; Liseno, Angelo. - (2010), pp. 1-5. (Intervento presentato al convegno 4rd Europ. Conf. on Antennas Prop. tenutosi a Barcelona, Spain nel Apr. 12-16, 2010).
A SVD-based approach to helicoidal NF-FF transformations
CAPOZZOLI, AMEDEO;CURCIO, CLAUDIO;D'ELIA, GIUSEPPE;LISENO, ANGELO
2010
Abstract
We propose a novel Near-Field-Far-Field (NF-FF) transformation technique for aperture antennas and cylindrical NF scanning. It exploits a proper aperture field representation, based on the use of the Prolate Spheroidal Wave Functions (PSWFs), accounting for the a priori information on shape and size of the Antenna Under Test (AUT). It furthermore allows a fast NF acquisition by employing a continuous and synchronized movement of probe and AUT, as well as on a helicoidal scan based on a nonredundant field representation. The NF-FF transformation problem is formulated as the inversion of a linear relation linking the unknown PSWF expansion coefficients to the NF samples and solved by a regularized Singular Value Decomposition (SVD) approach. Experimental results show the effectiveness of the approach, and how it enables a serious reduction of the measurement points without impairing the far-field estimation accuracy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.