An optimized near-field/far-field (NFFF) transformation for characterizing planar aperture antennas from “quasi-raster” and plane-polar multi-frequency scanning data is presented. The method is a generalization of that introduced by the Authors and then used for plane-polar and “quasi-raster” scans, respectively. The generalization consists of characterizing antennas at different frequencies by defining a common frequency-measurement grid to save scanning time. The method tackles the multi-frequency measurement problem by a linear operator, A, and solves the problem as a singular-value optimization of A. The field-sample positions are then chosen to provide the minimum number of near-field samples optimizing the singular-value dynamics of A. The computational burden is dealt with by proper programming on graphics processing units (GPUs). Numerical and experimental results show the effectiveness of the technique.
Multi-frequency planar near-field scanning by means of singular-value decomposition (SVD) optimization / Capozzoli, Amedeo; Curcio, Claudio; Liseno, Angelo. - In: IEEE ANTENNAS & PROPAGATION MAGAZINE. - ISSN 1045-9243. - 53:6(2011), pp. 212-221. [10.1109/MAP.2011.6157759]
Multi-frequency planar near-field scanning by means of singular-value decomposition (SVD) optimization
CAPOZZOLI, AMEDEO;CURCIO, CLAUDIO;LISENO, ANGELO
2011
Abstract
An optimized near-field/far-field (NFFF) transformation for characterizing planar aperture antennas from “quasi-raster” and plane-polar multi-frequency scanning data is presented. The method is a generalization of that introduced by the Authors and then used for plane-polar and “quasi-raster” scans, respectively. The generalization consists of characterizing antennas at different frequencies by defining a common frequency-measurement grid to save scanning time. The method tackles the multi-frequency measurement problem by a linear operator, A, and solves the problem as a singular-value optimization of A. The field-sample positions are then chosen to provide the minimum number of near-field samples optimizing the singular-value dynamics of A. The computational burden is dealt with by proper programming on graphics processing units (GPUs). Numerical and experimental results show the effectiveness of the technique.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.