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

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


Author(s):  
Brock J. LaMeres ◽  
Raymond J. Weber ◽  
Yikun Huang ◽  
Monther Abusultan ◽  
Sam Harkness

Author(s):  
Mudit Maheshwari ◽  
Sanchita Arora ◽  
Akhilesh M. Srivastava ◽  
Aditi Agrawal ◽  
Mahak Garg ◽  
...  

Author(s):  
Q. Yuan ◽  
T. Suguro ◽  
Q. Chen ◽  
K. Sawaya ◽  
E. Kudoh ◽  
...  

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