A method based on compressive sampling to achieve superresolution in ISAR imaging is presented. The superresolution
ISAR imaging algorithm is implemented by enforcing the sparsity constraints via random compressive
sampling of the measured data. Sparsity constraint ratio (SCR) is used as a design parameter. Mutual coherence is used
as a quantitative measure to determine the optimal SCR. ISAR data for full angular sector as well as different partial
angular sectors are utilized in this study. Results show that significant resolution enhancement is achieved around
optimal SCR of 0.2.
Multiple scattering and random interactions among scattering elements and between the scatterers and the background
adversely affect the radar image quality and target detection capability. In the radar image, multiple scattering and
interactions appear as non-physical scattering centers. A method to improve the performance of radar imaging systems
by extracting independent scattering centers is investigated in this study. Independent Component Analysis (ICA) is
applied to returns of a radar system to extract independent scattering centers based on their non-Gaussianity. As an
example, this method of target extraction is implemented in inverse synthetic aperture radar (ISAR) imaging of closely-spaced
targets. Results of this study show that the application of this radar signal processing technique has allowed
extraction of independent scattering centers which are needed in target detection and identification.
In radar imaging, for example Inverse Synthetic Aperture Radar (ISAR) imaging, a target can be modeled as a collection
of scattering centers in the image domain. A method to improve radar image quality through clutter suppression and
localization of scattering centers is presented in this paper. The approach is based on localizing the scattering centers by
enforcing sparsity constraints through random compressive sampling of the measured data. Sparsity constraint ratio is
chosen as a design parameter to achieve the objective. Results show that significant clutter reduction and improvement in
localization of scattering centers are achieved at an optimum sparsity constraint ratio.