volume | PIER Journals

2024-04-09

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

Kudret Akçapınar,

Mr. Kudret Akçapınar

Faculty of Engineering and Natural Sciences

Sabancı University

Turkey

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Naime Özben Önhon,

Dr. Naime Özben Önhon

Faculty of Engineering

Turkish-German University

Turkey

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Özgür Gürbüz,

Dr. Özgür Gürbüz

Faculty of Engineering and Natural Sciences

Sabancı University

Turkey

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Müjdat Çetin

Dr. Müjdat Çetin

Department of Electrical and Computer

University of Rochester

USA

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Progress In Electromagnetics Research C, Vol. 142, 183-193, 2024

doi:10.2528/PIERC24010805

Abstract

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, the 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 the 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.

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Kudret Akçapınar, Naime Özben Önhon, Özgür Gürbüz, and Müjdat Çetin, "Plug-and-Play ADMM Based Radar Range Profile Reconstruction Using Deep Priors," Progress In Electromagnetics Research C, Vol. 142, 183-193, 2024.
doi:10.2528/PIERC24010805

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