Warping-Driven Greedy Method for Data Reduction in Planar Near-Field Antenna Measurements

Near-field techniques for antenna testing can require the collection of a very large amount of data when the working frequency increases and/or the antenna is large in terms of wavelength. To mitigate such a drawback, a greedy method, which we call the warping-driven maximum noise propagation error (MNPE) algorithm, is introduced. Antenna under test (AUT) diagnostics is cast as the reconstruction of an equivalent surface current. Starting from a densely populated initial grid, the algorithm selects the sampling points one by one till the noise factor (NF), a figure that controls the propagation of noise from data to the source reconstruction and does not meet a slowly varying region. Hence, the procedure is arrested since no relevant further NF reduction would be obtained. It is shown that MNPE allows for a significant data reduction compared to other literature methods, especially when the starting grid is chosen according to the recently introduced warping sampling theory. Moreover, the initial grid selected according to the warping sampling, in turn, allows to dramatically reduce the computational cost of the selection procedure. A distinctive feature of the MNPE is that a priori information about the AUT can be easily included by projecting the unknown current onto properly selected base functions. As an example of that, we exploit the a priori information concerning the size of the source and the measurement aperture that leads to the radiation pattern being reliable within a bounded part of the visible spectrum (i.e., the so-called valid angle region). Extensive numerical analysis and a few experimental results pertaining to planar scanning confirm the effectiveness of the proposed method in dramatically reducing the amount of data without incurring performance degradation while estimating the source spatial spectrum.