Neural network-based pattern synthesis of array antennas with element specification

Author(s):  
R.G. Ayestarin ◽  
F. Las-Heras ◽  
L.F. Herran
1997 ◽  
Vol 33 (18) ◽  
pp. 1512 ◽  
Author(s):  
L. Landesa ◽  
F. Obelleiro ◽  
J.L. Rodríguez ◽  
A.G. Pino

Author(s):  
Tarek Sallam ◽  
Ahmed M. Attiya

Abstract Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.


1999 ◽  
Author(s):  
Ahmed H. El Zooghby ◽  
Christos G. Christodoulou ◽  
Michael Georgiopoulos

1998 ◽  
Vol 34 (16) ◽  
pp. 1540 ◽  
Author(s):  
L. Landesa ◽  
F. Obelleiro ◽  
J.L. Rodríguez ◽  
J.A. Rodríguez ◽  
F. Ares ◽  
...  

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