Adaptive Sampling in Spherical and Cylindrical Near-Field Antenna Measurements

Abstract

An adaptive approach for optimized sampling in cylindrical and spherical near-field antenna measurements is described. The presented technique applies higher sampling density in rapidly varying near-field regions, and skips data points in the smoother regions. Abrupt changes in the near field are detected by comparing the extrapolated and the measured near-field values at coarser sampling points during the measurements. A decision function, based on the signal-to-noise ratio of the measured value, is used to determine the threshold difference between the extrapolated and the measured near-field values for skipping the sampling point. The reduced near-field data collected is processed using the fast irregular antenna field transformation algorithm (FIAFTA). FIAFTA is computationally efficient, and capable of handling data on irregular grids with full probe correction. Several test cases are then presented related to the applicability of the given approach. A significant reduction in the number of measurement points was observed, thereby reducing measurement time and the computational effort.