scholarly journals Antenna Arrangement Verification for Low Sidelobe Levels

2019 ◽  
Author(s):  
Abigail J. Kragt Finnell ◽  
Peter J. Schubert
Keyword(s):  
Low Sidelobe

scholarly journals Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5164
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Antenna Array ◽  
Dynamic Range ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Single Step ◽  
Window Function ◽  
Spectral Domain ◽  
Low Sidelobe ◽  
Weighting Window

Recently, we proposed a Spectral Domain Sparse Representation (SDSR) approach for the direction-of-arrival estimation of signals incident to an antenna array. In the approach, sparse representation is applied to the conventional Bartlett spectra obtained from snapshots of the signals received by the antenna array to increase the direction-of-arrival (DOA) estimation resolution and accuracy. The conventional Bartlett spectra has limited dynamic range, meaning that one may not be able to identify the presence of weak signals in the presence of strong signals. This is because, in the conventional Bartlett spectra, uniform weighting (window) is applied to signals received by various antenna elements. Apodization can be used in the generation of Bartlett spectra to increase the dynamic range of the spectra. In Apodization, more than one window function is used to generate different portions of the spectra. In this paper, we extend the SDSR approach to include Bartlett spectra obtained with Apodization and to evaluate the performance of the extended SDSR approach. We compare its performance with a two-step SDSR approach and with an approach where Bartlett spectra is obtained using a low sidelobe window function. We show that an Apodization Bartlett-based SDSR approach leads to better performance with just single-step processing.

Design of a 94 GHz compact corrugated horn with ultra-low sidelobe

2016 ◽  
Author(s):  
Junbo Wang ◽  
Yuan Yao ◽  
Cheng Yang ◽  
Xiaoming Liu ◽  
Limei Qi ◽  
...  
Keyword(s):  
Low Sidelobe ◽  
Corrugated Horn

Evolutionary algorithms based synthesis of low sidelobe hexagonal arrays

2018 ◽  
Vol 38 ◽  
pp. 139-157 ◽  
Author(s):  
Sudipta Das ◽  
Rajesh Bera ◽  
Durbadal Mandal ◽  
Sakti Prasad Ghoshal ◽  
Rajib Kar
Keyword(s):  
Evolutionary Algorithms ◽  
Low Sidelobe ◽  
Hexagonal Arrays

scholarly journals Investigation of Low-Sidelobe Beampattern Controlling Methods for Acoustic Transmitting Array of Underwater Vehicles

2017 ◽  
Vol 24 (s2) ◽  
pp. 103-110
Author(s):  
Zhengyao He ◽  
Qiang Shi ◽  
Shaoxuan Wu
Keyword(s):  
Boundary Element ◽  
Element Model ◽  
Optimization Method ◽  
Underwater Vehicles ◽  
Unmanned Vehicles ◽  
Driving Voltage ◽  
Sound Waves ◽  
Low Sidelobe ◽  
Acoustic Transducer ◽  
Weighting Vector

Abstract In underwater unmanned vehicles, complex acoustic transducer arrays are always used to transmitting sound waves to detect and position underwater targets. Two methods of obtaining low-sidelobe transmitting beampatterns for acoustic transmitting arrays of underwater vehicles are investigated. The first method is the boundary element model optimization method which used the boundary element theory together with the optimization method to calculate the driving voltage weighting vector of the array. The second method is the measured receiving array manifold vector optimization method which used the measured receiving array manifold vectors and optimization method to calculate the weighting vector. Both methods can take into account the baffle effect and mutual interactions among elements of complex acoustic arrays. Computer simulation together with experiments are carried out for typical complex arrays. The results agree well and show that the two methods are both able to obtain a lower sidelobe transmitting beampattern than the conventional beamforming method, and the source level for each transmitting beam is maximized in constraint of the maximum driving voltage of array elements being constant. The effect of the second method performs even better than that of the first method, which is more suitable for practical application. The methods are very useful for the improvement of detecting and positioning capability of underwater unmanned vehicles.

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