SAR imaging via efficient implementations of sparse ML approaches

• Iterative sparse ML approaches are considered for spectral analysis in SAR imaging.
• Fast exact and approximated implementation algorithms are derived.
• Numerical and experimental examples illustrate the effectiveness of the method.

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.