Abstract
The theory of compressed sensing (CS) states that an unknown sparse signal can be accurately recovered from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, we present a new framework of high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) imaging based on CS. A phase-preserved CS approach for high-range resolution imaging is proposed. The phase of a Bi-ISAR signal can be extracted by constructing a phase-preserved Fourier basis, which is crucial to azimuth processing of Bi-ISAR imaging. After performing CS reconstruction in range, we present an improved version of CS-based cross-range imaging by combining modified Fourier basis and weighting with CS optimization. Simulated data are used to test the robustness of the Bi-ISAR imaging framework with two-dimensional (2-D) CS method. The results show that the framework is capable of accurate reconstruction of Bi-ISAR image in both range and cross-range.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
