Electromechanical coupling based performance evaluation of distorted phased array antennas with random position errors

2016 ◽  
Vol 51 (3) ◽  
pp. 285-295 ◽  
Author(s):  
Congsi Wang ◽  
Mingkui Kang ◽  
Wei Wang ◽  
Jianfeng Zhong ◽  
Yiqun Zhang ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Electromechanical Coupling ◽  
Phased Array ◽  
Phased Array Antennas ◽  
Position Errors ◽  
Array Antennas ◽  
Random Position

Electromechanical coupling modelling of distributed MEMS phase shifter for phased array antennas

2018 ◽  
Vol 105 (8) ◽  
pp. 1358-1373 ◽  
Author(s):  
Congsi Wang ◽  
Kang Ying ◽  
Haihua Li ◽  
Wei Gao ◽  
Lei Yin ◽  
...  
Keyword(s):  
Phase Shifter ◽  
Electromechanical Coupling ◽  
Phased Array ◽  
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.

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