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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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
Main Authors: Mingjiu, Lv, Chen, Wenfeng, Ma, Jianchao, Yang, Jun, Cheng, Qi, Ma, Xiaoyan
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary: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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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3191394