SAR Imaging via Efficient Implementations of Sparse ML Approaches

George-Othan Glentis, Kexin Zhao, Andreas Jakobsson, Habti Abeida, Jian Li

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

18 Citeringar (SciVal)
633 Nedladdningar (Pure)

Sammanfattning

High-resolution spectral estimation techniques are of notable interest for synthetic aperture radar (SAR) imaging. Several sparse estimation techniques have been shown to provide significant performance gains as compared to conventional approaches. We consider efficient implementation of the recent iterative sparse maximum likelihood-based approaches (SMLAs). Furthermore, we present approximative fast SMLA formulation using the Quasi-Newton approach, as well as consider hybrid SMLA-MAP algorithms. The effectiveness of the discussed techniques is illustrated using numerical and experimental examples.
Originalspråkengelska
Sidor (från-till)15-26
TidskriftSignal Processing
Volym95
UtgåvaFebruary
DOI
StatusPublished - 2014

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