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Bayesian compressive sensing in synthetic aperture radar imaging.

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Title: Bayesian compressive sensing in synthetic aperture radar imaging.
Authors: Xu, J.1, Pi, Y.1, Cao, Z.1
Source: IET Radar, Sonar & Navigation (Institution of Engineering & Technology). Jan2012, Vol. 6 Issue 1, p2-8. 7p. 1 Black and White Photograph, 2 Diagrams, 1 Chart, 4 Graphs.
Subjects: Synthetic aperture radar, Bayesian analysis, Radar, Imaging systems, Electronic systems, Remote sensing
Abstract: To achieve high-resolution two dimension images, synthetic aperture radar (SAR) with ultra wide-band faces considerably technical challenges such as long data collection time, huge amount of data storage and high hardware complexity. In these years, several imaging modalities based on compressive sensing (CS) have been proposed which can provide high-resolution images using significantly reduced number of samples. However, the CS-based methods are sensitive to noise and clutter. In this study, a new imaging modality based on Bayesian compressive sensing (BCS) is proposed along with a novel compressed sampling scheme. Clutter, which the previous CS-based methods not considered, is also included in this study. This new imaging scheme requires minor change to traditional system and allows both range and azimuth compressed sampling. Also, the Bayesian formalism accounts for additive noise encountered in the compressed measurement process. Experiments are carried out with noisy and cluttered imaging scenes to verify the new imaging scheme. The results indicate that the Bayesian formalism can provide a sharp and sparse image absence of side-lobes, which is the common problem in conventional imaging methods and has fewer artifacts compared with the previous version of CS-based methods. [ABSTRACT FROM AUTHOR]
Copyright of IET Radar, Sonar & Navigation (Institution of Engineering & Technology) is the property of Institution of Engineering & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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DbLabel: Engineering Source
An: 70565877
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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IllustrationInfo
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  Data: Bayesian compressive sensing in synthetic aperture radar imaging.
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  Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Radar%22">Radar</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems%22">Imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+systems%22">Electronic systems</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink>
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  Data: To achieve high-resolution two dimension images, synthetic aperture radar (SAR) with ultra wide-band faces considerably technical challenges such as long data collection time, huge amount of data storage and high hardware complexity. In these years, several imaging modalities based on compressive sensing (CS) have been proposed which can provide high-resolution images using significantly reduced number of samples. However, the CS-based methods are sensitive to noise and clutter. In this study, a new imaging modality based on Bayesian compressive sensing (BCS) is proposed along with a novel compressed sampling scheme. Clutter, which the previous CS-based methods not considered, is also included in this study. This new imaging scheme requires minor change to traditional system and allows both range and azimuth compressed sampling. Also, the Bayesian formalism accounts for additive noise encountered in the compressed measurement process. Experiments are carried out with noisy and cluttered imaging scenes to verify the new imaging scheme. The results indicate that the Bayesian formalism can provide a sharp and sparse image absence of side-lobes, which is the common problem in conventional imaging methods and has fewer artifacts compared with the previous version of CS-based methods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of IET Radar, Sonar & Navigation (Institution of Engineering & Technology) is the property of Institution of Engineering & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1049/iet-rsn.2010.0375
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      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 2
    Subjects:
      – SubjectFull: Synthetic aperture radar
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Radar
        Type: general
      – SubjectFull: Imaging systems
        Type: general
      – SubjectFull: Electronic systems
        Type: general
      – SubjectFull: Remote sensing
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      – TitleFull: Bayesian compressive sensing in synthetic aperture radar imaging.
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              Text: Jan2012
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              Y: 2012
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