Compressive sensing-based approach to the design of linear robust sparse antenna arrays with physical size constraint
In sparse arrays, the randomness of antenna locations avoids the introduction of grating lobes, while allowing adjacent antenna spacings to be greater than half a wavelength. This means a larger array size can be implemented using a relatively small number of antennas. However, careful consideration...
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Published in | IET microwaves, antennas & propagation Vol. 8; no. 10; pp. 736 - 746 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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Stevenage
The Institution of Engineering and Technology
01.07.2014
Institution of Engineering and Technology The Institution of Engineering & Technology |
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Abstract | In sparse arrays, the randomness of antenna locations avoids the introduction of grating lobes, while allowing adjacent antenna spacings to be greater than half a wavelength. This means a larger array size can be implemented using a relatively small number of antennas. However, careful consideration has to be given to antenna locations to ensure that an acceptable performance level is achieved. Model perturbations can also cause steering vector errors, which in turn cause discrepancies in the array's response, making robust arrays desirable. This study presents various compressive sensing-based methods that can solve this problem, while also imposing the antenna size as a constraint on the minimum adjacent antenna separations. Narrowband and multiband design examples are presented to verify the effectiveness of the proposed design methods, with comparisons being drawn with a previously proposed genetic algorithm-based approach. |
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AbstractList | In sparse arrays, the randomness of antenna locations avoids the introduction of grating lobes, while allowing adjacent antenna spacings to be greater than half a wavelength. This means a larger array size can be implemented using a relatively small number of antennas. However, careful consideration has to be given to antenna locations to ensure that an acceptable performance level is achieved. Model perturbations can also cause steering vector errors, which in turn cause discrepancies in the array's response, making robust arrays desirable. This study presents various compressive sensing-based methods that can solve this problem, while also imposing the antenna size as a constraint on the minimum adjacent antenna separations. Narrowband and multiband design examples are presented to verify the effectiveness of the proposed design methods, with comparisons being drawn with a previously proposed genetic algorithm-based approach. |
Author | Liu, Wei Hawes, Matthew B |
Author_xml | – sequence: 1 givenname: Matthew B surname: Hawes fullname: Hawes, Matthew B organization: Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK – sequence: 2 givenname: Wei surname: Liu fullname: Liu, Wei email: w.liu@sheffield.ac.uk organization: Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK |
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Keywords | antenna locations linear robust sparse antenna arrays compressed sensing adjacent antenna spacings antenna radiation patterns compressive sensing-based approach antenna arrays genetic algorithm-based approach physical size constraint Performance evaluation Sparse array Evolutionary algorithm Spacing Perturbation Narrow band Implementation Genetic algorithm Minimal distance Linear antenna arrays Localization Antenna array Comparative study Compressed sensing |
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SubjectTerms | Acceptability adjacent antenna spacings Antenna arrays antenna locations antenna radiation patterns Antennas Applied sciences Arrays compressed sensing compressive sensing‐based approach Exact sciences and technology genetic algorithm‐based approach Genetics linear robust sparse antenna arrays Mathematical analysis Narrowband Perturbation methods physical size constraint Radiocommunications Telecommunications Telecommunications and information theory |
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Title | Compressive sensing-based approach to the design of linear robust sparse antenna arrays with physical size constraint |
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