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

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2017-02-06

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Abstract

© 2017 Optical Society of America.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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10.1364/OE.25.002270

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Zhu, Ruoyu, Jonathan T Richard, David J Brady, Daniel L Marks and Henry O Everitt (2017). Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging. Optics Express, 25(3). pp. 2270–2284. 10.1364/OE.25.002270 Retrieved from https://hdl.handle.net/10161/14341.

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Brady

David J. Brady

Michael J. Fitzpatrick Distinguished Professor Emeritus of Photonics

David Brady leads the Duke Information Spaces Project (DISP). Historically, DISP has focused on computational imaging systems, with particular emphasis on smart cameras for security, consumer, transportation and broadcast applications. Currently DISP focuses primarily on the use of artificial intelligence in camera arrays for interactive broadcasting.


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