Fast and Accurate Near-Field to Far-Field Transformation Using an Adaptive Sampling Algorithm and Machine Learning

Alavi, Rezvan Rafiee; Mirzavand, Rashid; Mousavi, Pedram

doi:10.1109/apusncursinrsm.2019.8888868

Cited by 4 publications

(1 citation statement)

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“…A recently proposed method based on recursive partitioning in a multi-level subdomain hierarchy of radiating surfaces is applied to arbitrary surface measurements [22]. The authors in [23][24][25] described the adaptive method to reduce the measurement burden spherical near-field measurements. The fast irregular antenna field transformation algorithm (FIAFTA) was used to post-process the near field collected on an irregular grid [26] and the source reconstruction method was used to calculate the equivalent current on the surface of the ellipsoid containing the AUT [23].…”

Section: Introductionmentioning

confidence: 99%

Combined Application of Partition Clustering Classification and Gerchberg-Papoulis Optimization Algorithm for Spherical Near Field Antenna Measurements

Peng

Ren

et al. 2023

ACES Journal

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An adaptive sampling and optimized extrapolation scheme for spherical near-field antenna testing is proposed. The method relies on the partition clustering classification algorithm and Voronoi classification to divide a small amount of initial data into subclasses and cells. The sampling density and rates of variation between adjacent sampling points are used as an overall metric function to evaluate the sampling dynamics at each location. Appropriate interpolation is performed in the highly dynamic region to increase the effective data in the near-field samples. The Gerchberg-Papoulis algorithm extrapolates the unnecessary interpolation region to improve the near-field sampling accuracy. This method uses a small amount of initial near-field sampled data for near-far field conversion to achieve the same precision as uniform oversampling. The feasibility and stability of the algorithm are proved from the actual measurement results.

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