Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
Sparsity-driven synthetic aperture radar imaging: Reconstruction, autofocusing, moving targets, and compressed sensing
This article presents a survey of recent research on sparsity-driven synthetic aperture radar
(SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal …
(SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal …
An augmented Lagrangian method for complex-valued compressed SAR imaging
In this paper, we present a solution to the complex synthetic aperture radar (SAR) imaging
problem within a constrained optimization formulation where the objective function includes …
problem within a constrained optimization formulation where the objective function includes …
STLS-LADMM-Net: A deep network for SAR autofocus imaging
M Li, J Wu, W Huo, Z Li, J Yang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering
images of the illuminated area, playing a significant role in various applications. However …
images of the illuminated area, playing a significant role in various applications. However …
Compressed sensing SAR imaging based on centralized sparse representation
JC Ni, Q Zhang, Y Luo, L Sun - IEEE Sensors Journal, 2018 - ieeexplore.ieee.org
Sparse representation based synthetic aperture radar (SAR) imaging approaches have
shown their superior performance and great potential in compressed sensing SAR imaging …
shown their superior performance and great potential in compressed sensing SAR imaging …
Nonconvex-nonlocal total variation regularization-based joint feature-enhanced sparse SAR imaging
Z Xu, B Zhang, Z Zhang, M Wang… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) imaging under the sparse constraint is a developing SAR
imaging scheme that emerged in the recent decade.-norm and the total variation (TV)-norm …
imaging scheme that emerged in the recent decade.-norm and the total variation (TV)-norm …
Nonsparse SAR scene imaging network based on sparse representation and approximate observations
H Zhang, J Ni, K Li, Y Luo, Q Zhang - Remote Sensing, 2023 - mdpi.com
Sparse-representation-based synthetic aperture radar (SAR) imaging technology has shown
superior potential in the reconstruction of nonsparse scenes. However, many existing …
superior potential in the reconstruction of nonsparse scenes. However, many existing …
Joint SAR imaging and multi-feature decomposition from 2-D under-sampled data via low-rankness plus sparsity priors
In this paper, we introduce a multi-feature decomposition approach to the problem of
synthetic aperture radar (SAR) image reconstruction from under-sampled data in both range …
synthetic aperture radar (SAR) image reconstruction from under-sampled data in both range …
Sparse ISAR imaging using a greedy Kalman filtering approach
L Wang, O Loffeld, K Ma, Y Qian - Signal Processing, 2017 - Elsevier
The Compressive sensing (CS) theory provides a novel type of image reconstruction
methods for radar imaging. A good image can be obtained using much less data as …
methods for radar imaging. A good image can be obtained using much less data as …
Enhanced 1-bit radar imaging by exploiting two-level block sparsity
Conventional compressive sensing (CS) aims at sparse signal recovery from the
measurements with continuous values. Quantized CS (QCS) methods arise in digital …
measurements with continuous values. Quantized CS (QCS) methods arise in digital …