Adaptive beamforming technique based on neural network

2014 ◽  
pp. 227-232
Keyword(s):  
Neural Network ◽  
Adaptive Beamforming ◽  
Beamforming Technique

Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition

2016 ◽  
Author(s):  
Bo Li ◽  
Tara N. Sainath ◽  
Ron J. Weiss ◽  
Kevin W. Wilson ◽  
Michiel Bacchiani
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Adaptive Beamforming

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.

scholarly journals EVALUATION OF A NEURAL-NETWORK-BASED ADAPTIVE BEAMFORMING SCHEME WITH MAGNITUDE-ONLY CONSTRAINTS

2009 ◽  
Vol 11 ◽  
pp. 1-14 ◽  
Author(s):  
Giuseppe Castaldi ◽  
Vincenzo Galdi ◽  
Giampiero Gerini
Keyword(s):  
Neural Network ◽  
Adaptive Beamforming

scholarly journals Adaptive Beamforming Technique for Accurate Vertical Wind Measurements with Multichannel MST Radar

2012 ◽  
Vol 29 (12) ◽  
pp. 1769-1775 ◽  
Author(s):  
Koji Nishimura ◽  
Takuji Nakamura ◽  
Toru Sato ◽  
Kaoru Sato
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Beamforming ◽  
Vertical Wind ◽  
Constrained Minimization ◽  
Horizontal Wind ◽  
Signal To Noise ◽  
Mst Radar ◽  
Vertical Beam ◽  
Noise Ratio ◽  
Beamforming Technique

Abstract Aspect-sensitive backscattering of the atmosphere causes a small error in an effective line-of-sight direction in vertical beam observations leading to a serious degradation of vertical wind estimates due to contamination by horizontal wind components. An adaptive beamforming technique for a multichannel mesosphere–stratosphere–troposphere (MST) radar is presented, which makes it possible to measure the vertical wind velocity with higher accuracy by adaptively generating a countersteered reception beam against an off-vertically shifted echo pattern. The technique employs the norm-constrained direction-constrained minimization of power (NC-DCMP) algorithm, which provides not only robustness but also higher accuracy than the basic direction-constrained minimization of power algorithm in realistic conditions. Although the technique decreases the signal-to-noise ratio, the ratio is controlled and bound at a specified level by the norm constraint. In the case that a decrease of −3 dB is acceptable in a vertical beam observation, for which usually a much higher signal-to-noise ratio is obtained than for oblique beams, the maximum contamination is suppressed to even for the most imbalanced aspect sensitivity.

Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays

1998 ◽  
Vol 46 (12) ◽  
pp. 1891-1893 ◽  
Author(s):  
A.H.El. Zooghby ◽  
C.G. Christodoulou ◽  
M. Georgiopoulos
Keyword(s):  
Neural Network ◽  
Antenna Arrays ◽  
Adaptive Beamforming ◽  
Two Dimensional
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