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    An Artificial Neural Network Based Target Angle Estimation Technique for FMCW MIMO Radars
    (Electromagnetics Academy, 2023) Akçapınar, Kudret; Önhon, Naime Özben; Gürbüz, Özgür
    —In this paper, an artificial neural network (ANN) based approach is proposed for the estimation of the target angle using Multiple Input Multiple Output (MIMO) radars operating in Frequency Modulated Continuous Wave (FMCW). The proposed technique operates in two stages, with the first stage being the formation of the range profile at each MIMO element via Discrete Fourier Transform (DFT) and the second stage being the estimation of the target azimuth angle via an artificial neural network. The range profile formed in the first stage is fed to the second stage as a single snapshot angle measurement. The performance of the proposed technique is apprised with other existing methods under different Signal-to-Noise Ratio (SNR) conditions and measurement model uncertainties. The simulations performed show that the learning capability of the model strongly hinges on SNR conditions, and the learning process is ameliorated as SNR in training data increases as anticipated. Under low SNR conditions, the proposed technique performs better than other techniques in terms of Mean Square Error (MSE). We have also shown that our solution remains unaffected by the model uncertainties as it fully relies on the calibration data, while the performance of the model-based angle estimation techniques dramatically degrades as the uncertainty in the underlying model grows. © 2023, Electromagnetics Academy. All rights reserved.
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    Öğe
    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.