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

Author(s):  
Rezvan Rafiee Alavi ◽  
Rashid Mirzavand ◽  
Pedram Mousavi
2017 ◽  
Vol 65 (10) ◽  
pp. 5492-5502 ◽  
Author(s):  
Alexander Paulus ◽  
Josef Knapp ◽  
Thomas F. Eibert

1990 ◽  
Vol 26 (22) ◽  
pp. 1886 ◽  
Author(s):  
E. van Lil ◽  
C. Cao ◽  
A. van de Capelle ◽  
K. Van't Klooster

1997 ◽  
Vol 16 ◽  
pp. 269-284 ◽  
Author(s):  
T. K. Sarkar ◽  
P. Petre ◽  
A. Taaghol ◽  
R. F. Harrington

2012 ◽  
Vol 10 ◽  
pp. 69-73 ◽  
Author(s):  
K. A. Yinusa ◽  
C. H. Schmidt ◽  
T. F. Eibert

Abstract. Near-field measurements are established techniques to obtain the far-field radiation pattern of an Antenna Under Test via near-field measurements and subsequent near-field far-field transformation. For measurements acquired in echoic environments, additional post-processing is required to eliminate the effects of multipath signals in the resulting far-field pattern. One of such methods models the measurement environment as a multiple source scenario whereby the collected near-field data is attributed to the AUT and some scattering centers in the vicinity of the AUT. In this way, the contributions of the AUT at the probe can be separated from those of the disturbers during the near-field far-field transformation if the disturber locations are known. In this paper, we present ways of modeling the scattering centers on equivalent surfaces such that echo suppression is possible with only partial or no information about the geometry of the scatterers.


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