Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning
AbstractInterferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data. To deal with this problem, this paper proposes a novel SA–InISAR imaging method, which jointly reconstructs 2-dimensional (2-D) ISAR images from different channels through multiple response sparse Bayesian learning (M-SBL), a modification of sparse Bayesian learning (SBL), to achieve sparse recovery for multiple measurement vectors (MMV). We note that M-SBL suffers a heavy computational burden because it involves large matrix inversion. A computationally efficient M-SBL is proposed, which, proceeding in a sequential manner to avoid the time-consuming large matrix inversion, is denoted as sequential multiple sparse Bayesian learning (SM-SBL). Thereafter, SM-SBL is introduced to InISAR imaging to simultaneously reconstruct the ISAR images from different channels. Numerous experimental results validate that the proposed SM-SBL-based InISAR imaging algorithm performs superiorly against the traditional single-channel sparse-signal recovery (SSR)-based InISAR imaging methods in terms of noise suppression, outlier reduction and 3-dimensional (3-D) geometry estimation. View Full-Text
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Zhang, S.; Liu, Y.; Li, X. Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning. Sensors 2017, 17, 2295.
Zhang S, Liu Y, Li X. Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning. Sensors. 2017; 17(10):2295.Chicago/Turabian Style
Zhang, Shuanghui; Liu, Yongxiang; Li, Xiang. 2017. "Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning." Sensors 17, no. 10: 2295.
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