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    Plug-and-Play ADMM Based Radar Range Profile Reconstruction Using Deep Priors
    (Electromagnetics Academy, 2024) Akçapınar, Kudret; Önhon, Naime Özben; Gürbüz, Özgür; Çetin, Müjdat
    Reconstructing a range profile from radar returns, which are both noisy and band-limited, presents a challenging and ill-posed inverse problem. Conventional reconstruction methods often involve employing matched filters in pulsed radars or performing a Fourier transform of the received signal in continuous wave radars. However, both of these approaches rely on specific models and model-based inversion techniques that may not fully leverage prior knowledge of the range profiles being reconstructed when such information is accessible. To incorporate prior distribution information of the range profile data into the reconstruction process, regularizers can be employed to encourage specific spatial patterns within the range profiles. Nevertheless, these regularizers often fall short in effectively capturing the intricate spatial correlations within the range profile data, or they may not readily allow for analytical minimization of the cost function. Recently, Alternating Direction Method of Multipliers (ADMM) framework has emerged as a means to provide a way of decoupling the model inversion from the regularization of the priors, enabling the incorporation of any desired regularizer into the inversion process in a plug-and-play (PnP) fashion. In this paper, we implement an ADMM framework to address the radar range profile reconstruction problem where we propose to employ a Convolutional Neural Network (CNN) as a regularization method for enhancing the quality of the inversion process which usually suffers from the ill-posed nature of the problem. We demonstrate the efficacy of deep learning networks as a regularization method within the ADMM framework through our simulation results. We assess the performance of the ADMM framework employing CNN as a regularizer and conduct a comparative analysis against alternative methods under different measurement scenarios. Notably, among the methods under investigation, ADMM with CNN as a regularizer stands out as the most successful method for radar range profile reconstruction. © 2024, Electromagnetics Academy. All rights reserved.
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    SAR moving object imaging using sparsity imposing priors
    (Springer, 2017) Özben Önhon, Naime; Çetin, Müjdat
    Synthetic aperture radar (SAR) returns from a scene with motion can be viewed as data from a stationary scene, but with phase errors due to motion. Based on this perspective, we formulate the problem of SAR imaging of motion-containing scenes as one of joint imaging and phase error compensation. The proposed method is based on the minimization of a cost function which involves sparsity-imposing regularization terms on the reflectivity field to be imaged, considering that it admits a sparse representation as well as on the spatial structure of the motion-related phase errors, reflecting the assumption that only a small percentage of the entire scene contains moving objects. To incorporate the spatial structure of the phase errors into the problem, we provide three different sparsity-enforcing prior terms. In order to achieve computational gains, we also present a two-step version of our approach, which first determines regions of interest that are likely to contain the moving objects and then applies our sparsity-driven approach for joint image reconstruction and autofocusing in such a spatially constrained setting. Our preliminary experiments demonstrate the effectiveness of this new moving target SAR imaging approach.
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    SAR moving target imaging using group sparsity
    (European Signal Processing Conference, EUSIPCO, 2013) Özben Önhon, Naime; Çetin, Müjdat
    SAR imaging of scenes containing moving targets results in defocusing in the reconstructed images if the SAR observation model used in imaging does not take the motion into account. SAR data from a scene with motion can be viewed as data from a stationary scene, but with phase errors due to motion. Based on this perspective, we formulate the moving target SAR imaging problem as one of joint imaging and phase error compensation. Based on the assumption that only a small percentage of the entire scene contains moving targets, phase errors exhibit a group sparse nature, when the entire data for all the points in the scene are handled together. Considering this structure of motion-related phase errors and that many scenes of interest admit sparse representation in SAR imaging, we solve this joint problem by minimizing a cost function which involves two nonquadratic regularization terms one of which is used to enforce the sparsity of the reflectivity field to be imaged and the other is used to exploit the group sparse nature of the phase errors. © 2013 EURASIP.
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    Sparsity-driven synthetic aperture radar imaging [reconstruction, autofocusing, moving targets, and compressed sensing]
    (Ieee-Inst Electrical Electronics Engineers Inc, 2014) Çetin, Müjdat; Stojanovic, Ivana; Özben Önhon, Naime; Varshney, Kush R.; Samadi, Sadegh; Karl, William Clem; Willsky, Alan S.
    This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging.In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.