Multi-Frequency Planar Near-Field Scanning by Means of Singular-Value Decomposition (SVD) Optimization

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 in [1], and then used in [2] and [3] 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.