Plane-polar near-field scanning by means of SVD optimization

A Near-Field/Far-Field (NFFF) transformation for characterizing planar aperture antennas from planepolar scanning data is presented. The method recasts the measurement problem as a linear operator one, and solves it as a Singular Value Optimization. The field sample positions are chosen to provide the minimum number of NF samples optimizing the singular value dymamics of the relevant operator. The available a priori information on the AUT is accommodated to limit the number of parameters needed for the characterization and the transformation is performed by a regularized Singular Value Decomposition (SVD) approach. Experimental results show the effectiveness of the technique in reducing the number of required samples.