Synthesis of Sparse Near-Field Focusing Antenna Arrays Based on Bayesian Compressive Sensing

2018 ◽  
Author(s):  
Zi Xuan Huang ◽  
Yu Jian Cheng
Keyword(s):  
Compressive Sensing ◽  
Antenna Arrays ◽  
Near Field

Modifications of active phased antenna arrays near-field diagnosis method based on compressive sensing

2019 ◽  
Vol 11 (7) ◽  
pp. 568-576 ◽  
Author(s):  
Grigory Kuznetsov ◽  
Vladimir Temchenko ◽  
Maxim Miloserdov ◽  
Dmitry Voskresenskiy
Keyword(s):  
Compressive Sensing ◽  
Antenna Arrays ◽  
Near Field ◽  
Practical Implementation ◽  
Phased Antenna Arrays ◽  
Two Factors ◽  
Minimization Procedure ◽  
Diagnosis Method ◽  
Proposed Modification

AbstractThis paper presents two modifications of compressive sensing (CS)-based approach applied to the near-field diagnosis of active phased arrays. CS-based antenna array diagnosis allows a significant reduction of measurement time, which is crucial for the characterization of electrically large active antenna arrays, e.g. used in synthetic aperture radar. However, practical implementation of this method is limited by two factors: first, it is sensitive to thermal instabilities of the array under test, and second, excitation reconstruction accuracy strongly depends on the accuracy of the elements of the measurement matrix. First proposed modification allows taking into account of thermal instability of the array by using an iterative ℓ1-minimization procedure. The second modification increases the accuracy of reconstruction using several simple additional measurements.

scholarly journals Support Vector Regression for the Modeling and Synthesis of Near-Field Focused Antenna Arrays

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1352 ◽  
Author(s):  
Rafael González Ayestarán
Keyword(s):  
Support Vector Regression ◽  
Antenna Arrays ◽  
Near Field ◽  
Computational Cost ◽  
Support Vector ◽  
Analysis Tool ◽  
Training Samples ◽  
Learning Capabilities ◽  
Novel Method ◽  
The Individual

The powerful support vector regression framework is proposed in a novel method for near-field focusing using antenna arrays. By using this machine-learning method, the set of weights required in the elements of an array can be calculated to achieve an assigned near-field distribution focused on one or more positions. The computational cost is concentrated in an initial training process so that the trained system is fast enough for applications where moving devices are involved. The increased learning capabilities of support vector machines allow using a reduced number of training samples. Thus, these training samples may be generated with a prototype or a convenient electromagnetic analysis tool, and hence realistic effects, such as coupling or the individual radiation patterns of the elements of the arrays, are accounted for. Illustrative examples are presented.

Near-Field Antenna Arrays for Steerable Sub-Wavelength Magnetic-Field Beams

2014 ◽  
Vol 62 (7) ◽  
pp. 3543-3556 ◽  
Author(s):  
Alon Ludwig ◽  
Costas D. Sarris ◽  
George V. Eleftheriades
Keyword(s):  
Magnetic Field ◽  
Antenna Arrays ◽  
Near Field ◽  
Sub Wavelength

scholarly journals Design of Non-Uniform Antenna Arrays for Improved Near-Field MultiFocusing

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 645 ◽  
Author(s):  
Rafael González-Ayestarán ◽  
Jana Álvarez ◽  
Fernando Las-Heras
Keyword(s):  
Internet Of Things ◽  
Antenna Arrays ◽  
Degrees Of Freedom ◽  
Geometric Model ◽  
Near Field ◽  
Uniform Structure ◽  
Arbitrary Distribution ◽  
Wireless Link ◽  
Distribution Of Elements

An extended method for Near-Field Multifocusing on antenna arrays, including the optimization of the locations for the elements of the array, is proposed. Multifocusing is gaining attention in recent years due to the growth of applications such as Internet of Things, or 5G, where a wireless link between a number of sensors and devices must be established, and energy or interference must be managed efficiently. Multifocusing requirements may be addressed by optimizing the feeding weights that must be applied to the elements of an array, but the proposed methodology also optimizes their locations, increasing the degrees of freedom by allowing a non-uniform structure for the array, leading to more efficient structures or better compliance with the specifications. Some experiments are presented to validate the method, showing that it is able to determine the weights and mesh of the array to fulfill the requirements, both obtaining an arbitrary distribution of elements or following a predefined geometric model.

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