Inverse synthetic aperture radar (ISAR) images can be obtained using digital video broadcasting-terrestrial (DVB-T)-based passive radars. However, television broadcast-transmitted signals offer poor range resolution for imaging purposes, because they have a narrower bandwidth with respect to those transmitted by a dedicated ISAR system. To reach finer range resolutions, signals composed of multiple DVB-T channels are required. Problems arise, however, because DVB-T channels are typically widely separated in the frequency domain. The gaps between channels produce high grating Manuscript lobes in the image domain when Fourier-based algorithms are used to form the ISAR image. In this paper, compressive sensing theory is investigated to address this problem because of its ability to reconstruct sparse signals by using incomplete measures. By solving an optimization problem under the constraint of signal sparsity, passive ISAR images can be obtained with strongly reduced grating lobes. Both simulation and experimental results are shown to demonstrate the validity of the proposed approach.
Passive bistatic inverse synthetic aperture radar (PB-ISAR) has been recently introduced to add an important capability to passive coherent location (PCL) systems. Although evidence of such capability has been provided, a theoretical background that supports such findings is needed to fully comprehend PB-ISAR imaging. This paper provides a full theoretical basis for PB-ISAR including a performance analysis in terms of spatial resolution. Examples with real data are also provided as case studies
The applicability of compressive sensing (CS) to radar imaging has been recently proven and its capability to construct reliable radar images from a limited set of measurements demonstrated. In this study, a common framework for inverse synthetic aperture radar (ISAR) imaging via CS is provided and a CS-based ISAR imaging method is proposed. The proposed method is tested for application such as image reconstruction from compressed data, resolution enhancement and image reconstruction from gapped data. The effectiveness of the proposed method is demonstrated on real datasets and the performance evaluated by means of image contrast
Inverse synthetic aperture radar (ISAR) is a well-known technique for obtaining high-resolution radar images. ISAR techniques have been successfully applied in the recent past in combination with pulsed coherent radar. In order to be more appealing to both civilian and military fields, imaging sensors are required to be low cost, low powered, and compact. Coherent pulsed radars do not account for these requirements as much as frequency modulated continuous wave (FMCW) radars. However, FMCW radars transmit a linear frequency modulated (LFM) sweep in a relatively long time interval when compared with the pulse length of a coherent pulse radar. During such an interval the assumption of stop&go is no longer valid, that is the target cannot be considered stationary during the acquisition of the entire sweep echo. Therefore, the target motion within the sweep must be taken into account. Such a problem is formulated and solved for ISAR systems, where the target is noncooperative and additional unknowns are added to the signal model. In the present work, the authors define a complete FMCW-ISAR received signal model, propose an ISAR image formation technique suitable for FMCW radar and derive the point spread function (PSF) of the imaging system. Finally, the proposed FMCW ISAR autofocusing algorithm is tested on simulated and real data
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