Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.

Section: As Formentioning

confidence: 99%

Section: Framework Of the Proposed Ganmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

Wang

Lu³

et al. 2022

“…The l 0 -norm optimization, such as orthogonal matching pursuit (OMP) [7] and smoothed L0 (SL0) [8] are sensitive to noise and prone to local optima. For l 1 -norm optimization, a fast iterative shrinkage-thresholding algorithm (FISTA) [9] and alternating direction method of multipliers (ADMM) [10] can guarantee the sparsest solution. However, these methods usually require careful tuning of the regularization parameters, which is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

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

Bai

Zhou

et al. 2022

We propose a deep learning architecture, dubbed Plug-and-play 2D ADMM-Net (PAN), by combining model-driven deep networks and data-driven deep networks for effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging with various signal-to-noise ratios (SNR) and incomplete data scenarios. First, a sparse observation model of 2D ISAR imaging is established, and a 2D ADMM algorithm is presented. On this basis, using the plug and play (PnP) technique, PnP 2D ADMM is proposed, by combining the 2D ADMM algorithm and the deep denoising network DnCNN. Then, we unroll and generalize the PnP 2D ADMM to the PAN architecture, in which all adjustable parameters in the reconstruction layers, denoising layers, and multiplier update layers are learned by end-to-end training through back-propagation. Experimental results showed that the PAN with a single parameter set can achieve noise-robust ISAR imaging with superior reconstruction performance on incomplete simulated and measured data under different SNRs.

“…The l 0 -norm optimization, e.g., orthogonal MP (OMP) [8] and smoothed l 0 -norm method [9], cannot guarantee that the solution is sparsest and may converge to the local minima. The l 1norm optimization, e.g., the fast iterative shrinkage-thresholding algorithm (FISTA) [10,11] and alternating direction method of multipliers (ADMM) [12,13], is the convex approximation of the l 0 -norm [14]. However, the regularization parameter directly affects the performance and how to determine its optimum value remains an open problem [4].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

Bai

Zhou

2021

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.