Coupled structural-electromagnetic modeling and analysis of hexagonal active phased array antennas with random errors

2016 ◽  
Vol 70 (5) ◽  
pp. 592-598 ◽  
Author(s):  
Congsi Wang ◽  
Mingkui Kang ◽  
Yan Wang ◽  
Wei Wang ◽  
Jingli Du
Keyword(s):  
Phased Array ◽  
Random Errors ◽  
Electromagnetic Modeling ◽  
Modeling And Analysis ◽  
Phased Array Antennas ◽  
Array Antennas

Optically Derived Signal Set For Phased Array Antennas

1987 ◽  
Author(s):  
M. G. Parent ◽  
L. Goldberg ◽  
P. D. Stilwell, Jr.
Keyword(s):  
Phased Array ◽  
Phased Array Antennas ◽  
Signal Set ◽  
Array Antennas

Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

2021 ◽  
pp. 1-7
Author(s):  
Tarek Sallam ◽  
Ahmed M. Attiya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phased Array ◽  
Weight Vector ◽  
Adaptive Beamforming ◽  
Phased Array Antenna ◽  
Phased Array Antennas ◽  
Array Antennas ◽  
Optimum Weight ◽  
High Computational Complexity

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.

Sign in / Sign up

Export Citation Format

Share Document