Neural network processing for adaptive array antennas

2003 ◽  
Author(s):  
C.G. Christodoulou ◽  
A.H. El Zooghby ◽  
M. Georgiopoulos
Keyword(s):  
Neural Network ◽  
Adaptive Array ◽  
Array Antennas ◽  
Network Processing

Multiple mobile user tracking with neural network-based adaptive array antennas

1999 ◽  
Author(s):  
Ahmed H. El Zooghby ◽  
Christos G. Christodoulou ◽  
Michael Georgiopoulos
Keyword(s):  
Neural Network ◽  
Mobile User ◽  
Adaptive Array ◽  
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.

Recovery of noisy pyroelectric-detector signals through neural-network processing

2005 ◽  
Vol 76 (5) ◽  
pp. 053104 ◽  
Author(s):  
Martín G. González ◽  
Alejandro L. Peuriot ◽  
Verónica B. Slezak ◽  
Guillermo D. Santiago
Keyword(s):  
Neural Network ◽  
Pyroelectric Detector ◽  
Network Processing
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