High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net

Li, Xiaoyong; Bai, Xueru; Zhou, Yujie; Zhou, Feng

doi:10.3390/rs14040901

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…[17] introduces a PnP ADMM framework for SAR image reconstruction, integrating convolutional neural networks (CNNs) as regularizers. Similarly, [18] and [19] employ this approach for sparse ISAR imaging. In [18], the PnP ADMM framework is extended to the so-called PAN architecture, where all parameters in the reconstruction, denoising, and multiplier update layers are learned through end-to-end training via backpropagation.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, [18] and [19] employ this approach for sparse ISAR imaging. In [18], the PnP ADMM framework is extended to the so-called PAN architecture, where all parameters in the reconstruction, denoising, and multiplier update layers are learned through end-to-end training via backpropagation. In [17], separate CNNs are trained for diverse measurement conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Akçapınar,

Özben Önhon,

Gürbüz

et al. 2024

PIER C

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Akçapınar,

Özben Önhon,

Gürbüz

et al. 2024

PIER C

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“…Refs. [43,44] combine model-based sparse reconstruction and data-driven deep-learning techniques to provide effective high-resolution 2D ISAR imaging under low Signal-to-Noise Ratio (SNR) and incomplete data conditions. Ref.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Enhanced ISAR-RID Imaging Method

Wang,

Dai,

Song

et al. 2023

Remote Sensing

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Inverse synthetic aperture radar (ISAR) imaging can be improved by processing Range-Instantaneous Doppler (RID) images, according to a method proposed in this paper that uses neural networks. ISAR is a significant imaging technique for moving targets. However, scatterers span across several range bins and Doppler bins while imaging a moving target over a large accumulated angle. Defocusing consequently occurs in the results produced by the conventional Range Doppler Algorithm (RDA). Defocusing can be solved with the time-frequency analysis (TFA) method, but the resolution performance is reduced. The proposed method provides the neural network with more details by using a string of RID frames of images as input. As a consequence, it produces better resolution and avoids defocusing. Furthermore, we have developed a positional encoding method that precisely represents pixel positions while taking into account the features of ISAR images. To address the issue of an imbalance in the ratio of pixel count between target and non-target areas in ISAR images, we additionally use the idea of Focal Loss to improve the Mean Squared Error (MSE). We conduct experiments with simulated data of point targets and full-wave simulated data produced by FEKO to assess the efficacy of the proposed approach. The experimental results demonstrate that our approach can improve resolution while preventing defocusing in ISAR images.

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“…Adaptive network parameter adjustments take place through the use of training data. The trained networks can thus be used to obtain scene targets given the echo signal, among which, particularly, the fully convolutional neural networks (FCNs) [25] and UNet [26] and deep residual networks [27] have been well utilized for image formation in sparse SAR and ISAR imaging [28][29][30]. (2) Model-driven approach: Aiming at avoiding iterations optimization and sophisticated regularization parameters turning, model-driven methods [31][32][33] are built based on deep unfolding techniques that stem from the standard linear optimization algorithms, including IHT/IST [31] and ADMM networks [32] and AMP networks [33].…”

Section: Introductionmentioning

confidence: 99%

Fast Frequency-Diverse Radar Imaging Based on Adaptive Sampling Iterative Soft-Thresholding Deep Unfolding Network

Zhao

Zhang

et al. 2023

Remote Sensing

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Frequency-diverse radar imaging is an emerging field that combines computational imaging with frequency-diverse techniques to interrogate the high-quality images of objects. Despite the success of deep reconstruction networks in improving scene image reconstruction from noisy or under-sampled frequency-diverse measurements, their reliance on large amounts of high-quality training data and the inherent uninterpretable features pose significant challenges in the design and optimization of imaging networks, particularly in the face of dynamic variations in radar operating frequency bands. Here, aiming at reducing the latency and processing burden involved in scene image reconstruction, we propose an adaptive sampling iterative soft-thresholding deep unfolding network (ASISTA-Net). Specifically, we embed an adaptively sampling module into the iterative soft-thresholding (ISTA) unfolding network, which contains multiple measurement matrices with different compressed sampling ratios. The outputs of the convolutional layers are then passed through a series of ISTA layers that perform a sparse coding step followed by a thresholding step. The proposed method requires no need for heavy matrix operations and massive amount of training scene targets and measurements datasets. Unlike recent work using matrix-inversion-based and data-driven deep reconstruction networks, our generic approach is directly adapted to multi-compressed sampling ratios and multi-scene target image reconstruction, and no restrictions on the types of imageable scenes are imposed. Multiple measurement matrices with different scene compressed sampling ratios are trained in parallel, which enables the frequency-diverse radar to select operation frequency bands flexibly. In general, the application of the proposed approach paves the way for the widespread deployment of computational microwave and millimeter wave frequency-diverse radar imagers to achieve real-time imaging. Extensive imaging simulations demonstrate the effectiveness of our proposed method.

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High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net

Cited by 6 publications

References 24 publications

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

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

Deep Learning-Based Enhanced ISAR-RID Imaging Method

Fast Frequency-Diverse Radar Imaging Based on Adaptive Sampling Iterative Soft-Thresholding Deep Unfolding Network

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