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Speaker: Dr. Rezvan Rafiee Alavi, DVTEST

Rezvan Rafiee Alavi (M’17) received the B.Sc. degree from the University of Tehran in 2009, in electrical engineering, and M.Sc. degree from Iran University of Science and Technology, in 2013, in telecommunication, fields and waves, and Ph.D. from University of Alberta, in 2019. Currently, she is a postdoctoral research fellow with the Intelligent Wireless Technology Laboratory (IWT), University of Alberta, and DVTEST. Inc. She is also a cofounder of Anteligen Company. She has authored more than 20 papers published in refereed journals and conferences proceedings and also three patents during her Ph.D. and postdoctoral research. Dr. Alavi was a recipient of Mary Louise Imrie Graduate Student Award, honorable mention award in APS/URSI student paper competition (2019). Her research interests include antenna and propagation, passive and active microwave circuits, numerical methods in electromagnetics, inverse electromagnetic scattering, remote sensing, antenna over the air (OTA) measurements, and the application of machine learning and artificial intelligence in antenna measurement and fault detection of antennas and microwave circuits

Abstract

Near-field to far-field transformation is a method for antenna characterization that computes the metrics defined in far-field by using mathematical transformation. This method makes it possible to have affordable very compact enclosures. Clearly, compared to direct and indirect far-field measurement methods, near-field to far-field transformation requires more near-field data correction and post-processing. This is because the distance between the probe and AUT is close, and also the collected data are used for further calculations. Therefore, any error in the collected data can propagate through all the calculations and decrease the accuracy. To correct the measured near-field data several correction methods are used as probe correction, phase center detection and correction, metallic parts and absorber effects removal. Furthermore, to reduce the measurement and post-processing time, instead of uniform sampling, an adaptive sampling technique is proposed and implemented. This method reduces the measurement and post-processing time to the quarter of the time of the uniform sampling. In this talk, I will present the algorithm and methods we have used for NFFF transformation, NF correction, uniform and adaptive NF data acquisition. We will have a live demo of our measurement setup, adaptive sampling method and Signal Shape software that we have developed for NFFF transformation.