Abstract
In this paper, a novel framework based on nonconvex constraint and nonlocal total variation (NLTV) regularization is proposed for sparsity-driven multistatic inverse synthetic aperture radar (ISAR) imaging under limited measurements. The nonconvex constraint measures sparsity more appropriately since it can better fit to the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm, and NLTV plays a better role in preserving the shape and geometry of target than the commonly used scattering points sparsity. An iterative optimization solution method for the proposed imaging model is also derived. Simulation results show the effectiveness of the proposed method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.