Beam shaping with antenna arrays using neural networks

2002 ◽  
Author(s):  
S. Reza ◽  
C.G. Chrostodulou
Keyword(s):  
Neural Networks ◽  
Antenna Arrays ◽  
Beam Shaping

Применение адаптивного анизотропного диффузного фильтра для повышения качества изображения отражателей при проведении ультразвукового неразрушающего контроля

2021 ◽  
pp. 3-12
Author(s):  
Е.Г. Базулин
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Antenna Arrays ◽  
Human Factor ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Efficient Operation ◽  
Optical Images ◽  
Segmentation Methods ◽  
Segmentation Of Images

Currently, in order to increase the speed of preparing the ultrasound control protocol and reduce the influence of the human factor, systems for recognizing (classifying) reflectors based on artificial neural networks are being actively developed. For their more efficient operation, the images of the reflectors need to be worked on in order to increase the signal-to-noise ratio of the image and its segmentation (clustering). One of the segmentation methods is to process the image with an adaptive anisotropic diffuse filter, which is used to process optical images. In model experiments, the effectiveness of using this texture filter for segmentation of images of reflectors reconstructed from echo signals measured using antenna arrays is demonstrated.

scholarly journals STUDY OF UNEQUALLY-EXCITED RANDOM ANTENNA ARRAYS FOR BEAM SHAPING

2018 ◽  
Vol 85 ◽  
pp. 129-140 ◽  
Author(s):  
Giovanni Buonanno ◽  
Raffaele Solimene
Keyword(s):  
Antenna Arrays ◽  
Beam Shaping

scholarly journals Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex

2019 ◽  
Vol 216 ◽  
pp. 02003
Author(s):  
D. Shipilov ◽  
P.A. Bezyazeekov ◽  
N.M. Budnev ◽  
D. Chernykh ◽  
O. Fedorov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Antenna Arrays ◽  
Signal Reconstruction ◽  
Cosmic Ray ◽  
Matched Filtering ◽  
Background Suppression ◽  
Signal Recognition ◽  
Air Showers ◽  
Matched Filters ◽  
Prospects Of Application

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km2. In the present workwe discuss the improvements of the signal reconstruction applied for Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performanceof matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised traces, i.e. removes all signal-unrelated amplitudes. We present the comparison between the standard method of signal reconstruction, matched filtering and the autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection.

scholarly journals Design and analysis of Maxwell fisheye lens based beamformer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Ali Babar Abbasi ◽  
Rafay I. Ansari ◽  
Gabriel G. Machado ◽  
Vincent F. Fusco
Keyword(s):  
Antenna Arrays ◽  
Secure Communication ◽  
Beam Steering ◽  
Finite Size ◽  
Beam Shaping ◽  
Hardware Architecture ◽  
Fisheye Lens ◽  
Communication Links ◽  
Feed Network ◽  
Wireless Coverage

AbstractAntenna arrays and multi-antenna systems are essential in beyond 5G wireless networks for providing wireless connectivity, especially in the context of Internet-of-Everything. To facilitate this requirement, beamforming technology is emerging as a key enabling solution for adaptive on-demand wireless coverage. Despite digital beamforming being the primary choice for adaptive wireless coverage, a set of applications rely on pure analogue beamforming approaches, e.g., in point-to-multi point and physical-layer secure communication links. In this work, we present a novel scalable analogue beamforming hardware architecture that is capable of adaptive 2.5-dimensional beam steering and beam shaping to fulfil the coverage requirements. Beamformer hardware comprises of a finite size Maxwell fisheye lens used as a scalable feed network solution for a semi-circular array of monopole antennas. This unique hardware architecture enables a flexibility of using 2 to 8 antenna elements. Beamformer development stages are presented while experimental beam steering and beam shaping results show good agreement with the estimated performance.

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