Bayesian Inverse Synthetic Aperture Radar Imaging by Exploiting Sparse Probing Frequencies

This letter proposes a Bayesian compressed sensing (BCS) method for high-resolution inverse synthetic aperture radar (ISAR) imaging when the basis matrix depends on an unknown rotation rate. In the proposed method, the radar transmits only a few probing frequencies instead of a wideband signal. In d...

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Bibliographic Details
Published inIEEE antennas and wireless propagation letters Vol. 14; pp. 1698 - 1701
Main Authors Wang, Bin, Zhang, Shunsheng, Wang, Wen-Qin
Format Journal Article
LanguageEnglish
Published IEEE 2015
Subjects
Basis matrix
Bayes methods
Bayesian compressed sensing (BCS)
Compressed sensing
Image reconstruction
Image resolution
Imaging
inverse synthetic aperture radar (ISAR)
Matching pursuit algorithms
Radar imaging
rotation rate
sparse probing frequencies (SPF)
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Summary:This letter proposes a Bayesian compressed sensing (BCS) method for high-resolution inverse synthetic aperture radar (ISAR) imaging when the basis matrix depends on an unknown rotation rate. In the proposed method, the radar transmits only a few probing frequencies instead of a wideband signal. In doing so, both the rotation rate and a high-resolution ISAR image can be simultaneously retrieved by jointly applying the BCS algorithm using Gaussian prior and a gradient-based search algorithm to exploit the sparse probing frequencies. The effectiveness of the proposed method is verified by experimental results with both simulated data and real data.
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2015.2419275