SAR Imaging via Efficient Implementations of Sparse ML Approaches

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.

Zhao, Kexin (författare)
Jakobsson, AndreasLund University,Lunds universitet,Biomedical Modelling and Computation,Forskargrupper vid Lunds universitet,Statistical Signal Processing Group,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)mats-aaj (författare)
Abeida, Habti (författare)
Li, Jian (författare)
Biomedical Modelling and ComputationForskargrupper vid Lunds universitet (creator_code:org_t)

Av författaren/redakt...: Glentis, George- ...; Zhao, Kexin; Jakobsson, Andre ...; Abeida, Habti; Li, Jian

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