Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging

Ruoyu Zhu; Jonathan T. Richard; David J. Brady; Daniel L. Marks; Henry O. Everitt

doi:10.1364/OE.25.002270

Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging

Ruoyu Zhu
, Jonathan T. Richard
, David J. Brady
, Daniel L. Marks
, Henry O. Everitt

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.

Original language	English (US)
Pages (from-to)	2270-2284
Number of pages	15
Journal	Optics Express
Volume	25
Issue number	3
DOIs	https://doi.org/10.1364/OE.25.002270
State	Published - Feb 6 2017
Externally published	Yes

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics

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10.1364/OE.25.002270

Cite this

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title = "Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging",

abstract = "In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.",

author = "Ruoyu Zhu and Richard, \{Jonathan T.\} and Brady, \{David J.\} and Marks, \{Daniel L.\} and Everitt, \{Henry O.\}",

note = "Publisher Copyright: {\textcopyright} 2017 Optical Society of America.",

year = "2017",

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doi = "10.1364/OE.25.002270",

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AU - Zhu, Ruoyu

AU - Richard, Jonathan T.

AU - Brady, David J.

AU - Marks, Daniel L.

AU - Everitt, Henry O.

PY - 2017/2/6

Y1 - 2017/2/6

N2 - In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.

AB - In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.

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UR - https://www.scopus.com/pages/publications/85011579935#tab=citedBy

U2 - 10.1364/OE.25.002270

DO - 10.1364/OE.25.002270

M3 - Article

C2 - 29519075

AN - SCOPUS:85011579935

SN - 1094-4087

VL - 25

SP - 2270

EP - 2284

JO - Optics Express

JF - Optics Express

IS - 3

ER -

Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging

Abstract

ASJC Scopus subject areas

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