Compressed sensing approach for pattern synthesis of maximally sparse non-uniform linear array

Compressed sensing (CS) has been successfully applied to the synthesis of maximally sparse non-uniform linear array with the synthesised pattern matching the reference pattern very well by using as few elements as possible. According to the CS theory, a sparse or compressible high-dimensional signal...

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Bibliographic Details
Published in:	IET microwaves, antennas & propagation Vol. 8; no. 5; pp. 301 - 307
Main Authors:	Zhao, Xiaowen, Yang, Qingshan, Zhang, Yunhua
Format:	Journal Article
Language:	English
Published:	Stevenage The Institution of Engineering and Technology 01.04.2014 Institution of Engineering and Technology The Institution of Engineering & Technology
Subjects:	Algorithms antenna arrays Antennas Applied sciences Compressed compressed sensing compressed sensing approach compressed sensing theory compressible high‐dimensional signal Computer simulation Detection Exact sciences and technology Linear arrays low‐dimensional information low‐dimensional space Mathematical models maximally sparse nonuniform linear array measurement matrix Microwave antennas numerical analysis numerical simulations pattern synthesis Radiocommunications sparse antenna array sparse linear arrays sparse‐array synthesis methods Synthesis synthesised pattern matching Telecommunications Telecommunications and information theory sparse antenna array numerical simulations compressed sensing measurement matrix synthesised pattern matching low-dimensional information sparse linear arrays sparse-array synthesis methods compressed sensing theory pattern synthesis maximally sparse nonuniform linear array compressed sensing approach antenna arrays numerical analysis low-dimensional space compressible high-dimensional signal Performance evaluation Sparse array Low-dimensional system Linear array Pattern recognition Algorithm Implementation Numerical simulation Linear antenna arrays Pattern matching Pattern synthesis Compressed sensing Antenna synthesis
ISSN:	1751-8725, 1751-8733
Online Access:	Get full text
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