Gridless Sparse ISAR Imaging via 2-D Fast Reweighted Atomic Norm Minimization
Aiming at acquiring high-resolution ISAR image effectively and quickly, a new fast gridless imaging method with a sound two-dimensional (2-D) reweighting strategy is proposed in this letter. First, the received echo is characterized as a weighted linear combination of 2-D frequencies chosen from a m...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters Jg. 19; S. 1 - 5 |
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2022
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| Abstract | Aiming at acquiring high-resolution ISAR image effectively and quickly, a new fast gridless imaging method with a sound two-dimensional (2-D) reweighting strategy is proposed in this letter. First, the received echo is characterized as a weighted linear combination of 2-D frequencies chosen from a matrix-form atom set, forming a new nonconvex optimization model for 2-D gridless ISAR imaging based on the 2-D atomic norm minimization (2-D ANM) framework. Next, a reweighting optimization strategy is adopted, which iteratively carries out the 2-D ANM to determine the preference of 2-D frequencies selection based on the latest estimation, to enhance sparsity and resolution. Furthermore, a feasible algorithm based on alternating direction method of multipliers (ADMM) is used in each iteration to further decrease the computational complexity. Once the optimization problem is solved, the 2-D frequencies encoded in two one-level Toeplitz matrices can be obtained using the Vandermonde decomposition (VD). Numerical experiments demonstrate that the proposed method is able to achieve high-resolution ISAR image, while it has a remarkable computational efficiency. |
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| AbstractList | Aiming at acquiring high-resolution ISAR image effectively and quickly, a new fast gridless imaging method with a sound two-dimensional (2-D) reweighting strategy is proposed in this letter. First, the received echo is characterized as a weighted linear combination of 2-D frequencies chosen from a matrix-form atom set, forming a new nonconvex optimization model for 2-D gridless ISAR imaging based on the 2-D atomic norm minimization (2-D ANM) framework. Next, a reweighting optimization strategy is adopted, which iteratively carries out the 2-D ANM to determine the preference of 2-D frequencies selection based on the latest estimation, to enhance sparsity and resolution. Furthermore, a feasible algorithm based on alternating direction method of multipliers (ADMM) is used in each iteration to further decrease the computational complexity. Once the optimization problem is solved, the 2-D frequencies encoded in two one-level Toeplitz matrices can be obtained using the Vandermonde decomposition (VD). Numerical experiments demonstrate that the proposed method is able to achieve high-resolution ISAR image, while it has a remarkable computational efficiency. |
| Author | Cheng, Qi Chen, Wenfeng Ma, Jianchao Ma, Xiaoyan Yang, Jun Mingjiu, Lv |
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| Cites_doi | 10.1109/JSEN.2018.2868308 10.1109/TPAMI.2017.2689021 10.1109/TIT.2018.2881113 10.1109/TSP.2014.2386283 10.1080/2150704X.2016.1192699 10.1109/ACC.2003.1243393 10.1016/j.sigpro.2019.04.024 10.1109/TGRS.2019.2893505 10.1049/iet-spr.2017.0366 10.1109/TSP.2015.2493987 10.1186/s13634-016-0379-2 10.1109/TAES.2019.2895717 10.1049/iet-rsn.2018.5181 10.1109/TIT.2013.2277451 10.1109/SAM.2016.7569639 |
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| SubjectTerms | Algorithms Alternating direction method of multipliers (ADMM) Apertures atomic norm minimization (ANM) Computer applications Echoes High resolution Image acquisition Image resolution Imaging Imaging techniques inverse synthetic aperture radar (ISAR) Iterative methods Mathematical analysis Methods Minimization Numerical experiments off grid Optimization Optimization models Radar imaging Resolution Sparse matrices stepped frequency signal Two dimensional models |
| Title | Gridless Sparse ISAR Imaging via 2-D Fast Reweighted Atomic Norm Minimization |
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