Plug-and-Play ADMM Based Radar Range Profile Reconstruction Using Deep Priors

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dc.contributor.authorAkçapınar, Kudret
dc.contributor.authorÖnhon, Naime Özben
dc.contributor.authorGürbüz, Özgür
dc.contributor.authorÇetin, Müjdat
dc.date.accessioned2025-02-20T08:46:31Z
dc.date.available2025-02-20T08:46:31Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractReconstructing 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.
dc.identifier.doi10.2528/PIERC24010805
dc.identifier.endpage193en_US
dc.identifier.issn1937-8718
dc.identifier.scopus2-s2.0-85191497386
dc.identifier.scopusqualityQ3
dc.identifier.startpage183en_US
dc.identifier.urihttps://doi.org/10.2528/PIERC24010805
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1770
dc.identifier.volume142en_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElectromagnetics Academy
dc.relation.ispartofProgress In Electromagnetics Research C
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250220
dc.subjectConvolutionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectCost functionsen_US
dc.subjectDeep learningen_US
dc.subjectInverse problemsen_US
dc.subjectKnowledge managementen_US
dc.subjectLearning systemsen_US
dc.subjectAlternating directions method of multipliersen_US
dc.subjectConvolutional neural networken_US
dc.subjectInversion processen_US
dc.subjectPlug-and-playen_US
dc.subjectProfile dataen_US
dc.subjectProfile Reconstructionen_US
dc.subjectRadar range profileen_US
dc.subjectRange-profilesen_US
dc.subjectRegularization methodsen_US
dc.subjectRegularizeren_US
dc.subjectMatched filtersen_US
dc.titlePlug-and-Play ADMM Based Radar Range Profile Reconstruction Using Deep Priors
dc.typeArticle

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