3-D Scattering Image Sparse Reconstruction via Radar Network

Kang, Le; Luo, Ying; Zhang, Qun; Liu, Xiao-Wen; Liang, Bi-shuai

doi:10.1109/tgrs.2020.3039351

Cited by 20 publications

(9 citation statements)

References 26 publications

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“…satisfies: The gradient calculations of q 1) , and X (n) are consistent with the derivation of the piecewise linear function defined in [29].…”

supporting

confidence: 73%

“…Step1: Training set generation. Initialize the phase error matrix E = I, construct Φ 1 and Φ 2 according to the radar parameters and data missing pattern, generate randomly distributed scattering centers X gt with Gaussian amplitudes, calculate Y according to (1), and obtain the data set Γ = Y, X gt .…”

Section: D High-resolution Isar Imaging Based On 2d-adnmentioning

confidence: 99%

“…High-resolution inverse synthetic aperture radar (ISAR) imaging plays a significant role in space situation awareness and air target surveillance [1,2]. Under ideal observational environments with high signal-to-noise ratios (SNRs) and complete echo matrices, wellfocused imaging can be acquired by classic techniques such as the range-Doppler (RD) algorithm and the polar formatting algorithm (PFA) [3].…”

Section: Introductionmentioning

confidence: 99%

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

Bai

Zhou

2021

Remote Sensing

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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.

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“…satisfies: The gradient calculations of q 1) , and X (n) are consistent with the derivation of the piecewise linear function defined in [29].…”

supporting

confidence: 73%

Section: D High-resolution Isar Imaging Based On 2d-adnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bai

Zhou

2021

Remote Sensing

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“…where Re(•) and Im(•) denote taking the real and the imaginary parts, respectively. By this means, PAN can properly deal with the complex-valued matrix multiplication in (6). Implementation Details: The adjustable parameters of PAN were initialized as ρ (n) = 0.2 and η (n) = 1.…”

Section: Other Detailsmentioning

confidence: 99%

“…Additionally, in complex electromagnetic environments with active interference and passive interference, the non-cooperative nature of target and radar resource scheduling [4] may result in incomplete echoes, and the available imaging algorithms based on Fourier analysis cannot obtain satisfactory results. Considering the sparse nature of the scattering centers in the image domain, high-resolution ISAR imaging based on sparse signal reconstruction has received intensive attention in recent years [5,6].…”

Section: Introductionmentioning

confidence: 99%

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

Bai

Zhou

et al. 2022

Remote Sensing

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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.

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3D inverse synthetic aperture radar image quality improvement using sparse signal representation

Mehrpooya

Karbasi

Nazari

et al. 2022

IET Radar Sonar & Navi

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Generalisation of one‐dimensional dictionary learning (1DDL) algorithms to Multidimensional (MD) mode and its utilisation in MD data applications, increases the speed and reduces the computational complexity. An example of such an application is 3D inverse synthetic aperture radar (ISAR) image reconstruction and noise reduction. In this study, in addition to MD mode generalisation, the formulation structure of the multidimensional dictionary learning (MDDL) problem is discussed followed by two novel algorithms to solve it. The first one is based on the K‐singular value decomposition algorithm for 1DDL, which uses alternating minimisation and singular value decomposition. The second algorithm is the extension of the sequential generalisation of K‐means 1DDL algorithm to the MD mode. Moreover, the MD tensor denoising method based on the MDDL algorithm (MDDL‐ALG) is proposed. As an application, the proposed method is used to denoise the 3D ISAR image. The numerical simulations reveal that the proposed methods, in addition to reducing the memory consumption and the computational complexity, also enjoy higher convergence rate in comparison to 1D algorithms. Specifically, convergence speed of MD algorithms, depending on the training data size, is up to at least 10 times faster than the equivalent 1D counterparts. As revealed through the simulations, the amount of signal to noise ratio recovered by the proposed methods is almost 2 dB higher than the case using a pre‐designed dictionary for denoising. Moreover, it outperforms about 10 dB over the case with the conventional 3D‐IFFT method for image construction.

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3-D Scattering Image Sparse Reconstruction via Radar Network

Cited by 20 publications

References 26 publications

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

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

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

3D inverse synthetic aperture radar image quality improvement using sparse signal representation

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