An adaptive approach to minimize acquisition time in planar near-field antenna measurements is described. In contrast to the traditional planar near-field scanning, the presented technique acquires the near-field in form of rectangular rings and skips sampling points in smoothly varying near-field regions. Abrupt changes in the near-field are detected by comparing extrapolated and measured near-field values at coarser sampling points. A decision function based on the signal-to-noise ratio (SNR) of the measured value is used to determine the threshold difference between the measured and the extrapolated near-field values for skipping the sampling point. Near-field data thus collected on the resultant irregular grid is processed using the multilevel plane wave based near-field far-field transformation algorithm. The multilevel transformation algorithm is computationally efficient and capable of handling data collected on irregular grids. A rigorous analysis of the adaptive data acquisition approach is then performed in terms of transformed far-field accuracy, decision factor, and test time reduction. Several test cases covering a variety of antennas are shown using synthetic as well as measured data for realistic results. Afterwards the acquisition time for the worst case scenario is compared with the traditional planar near-field measurement technique.
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