Wideband phased array radar with digital adaptive beamforming

1999 ◽  
Author(s):  
C. Tarran
Keyword(s):  
Phased Array ◽  
Adaptive Beamforming ◽  
Phased Array Radar

scholarly journals Aspects of the Subarrayed Array Processing for the Phased Array Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Hang Hu
Keyword(s):  
Phased Array ◽  
Array Processing ◽  
Direction Finding ◽  
Adaptive Beamforming ◽  
Fast Time ◽  
Configuration Optimization ◽  
Numerical Examples ◽  
Phased Array Radar ◽  
Time Space ◽  
Space Time Adaptive Processing

This paper gives an overview on the research status, developments, and achievements of subarrayed array processing for the multifunction phased array radar. We address some issues concerning subarrayed adaptive beamforming, subarrayed fast-time space-time adaptive processing, subarray-based sidelobe reduction of sum and difference beam, subarrayed adapted monopulse, subarrayed superresolution direction finding, subarray configuration optimization in ECCM (electronic counter-countermeasure), and subarrayed array processing for MIMO-PAR. In this review, several viewpoints relevant to subarrayed array processing are pointed out and the achieved results are demonstrated by numerical examples.

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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