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

Abstract

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.

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

“…The corresponding HR ISAR images can be acquired by convolving target scattering function with PSF. Here, the PSF is approximated by a 2-D Gaussian function instead of the Sinc function and its expression is shown in Equation (19):…”

“…x y y x h x y (19) where σ 2 x and σ 2 y control the azimuth and range resolution, respectively. Then under the condition of no noise and full aperture, the LR ISAR images and HR ISAR images are the inputs and annotations of the proposed GAN, respectively.…”

“…With the same purpose, a joint approximate message-passing (AMP) and phase error estimation method is proposed in [18] and the corresponding deep learning network is constructed. In [19], a 2D ADMM method is unrolled to form a deep network for high-resolution 2D ISAR imaging and autofocusing under the condition of a low signal-to-noise ratio (SNR) and SA.…”

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

“…As a comparison, we provide imaging results of the 2D-ADN [16], UNet [19], and PnP 2D ADMM, respectively. According to the analysis given in Section 1, we needed to build a model set for the noise-level dependent, model-driven 2D-ADN, as the SNR is varying among echoes.…”

“…Finally, they output the focused image of an unknown target from the trained network. Typical networks with modeldriven methods include AF-AMPNet [15], 2D-ADMM-Net (2D-ADN) [16], and convolution iterative shrinkage-thresholding (CIST) [17], etc. Although model-driven methods have strong interpretability and satisfying reconstruction performance, the optimal parameters are sensitive to SNR.…”