Computationally Efficient Sparse Aperture ISAR Autofocusing and Imaging Based on Fast ADMM

In the case of sparse aperture, the coherence between pulses of radar echo is destroyed, which challenges inverse synthetic aperture radar (ISAR) autofocusing and imaging. Mathematically, reconstructing the ISAR image from the sparse aperture radar echo is a linear underdetermined inverse problem, w...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 58; číslo 12; s. 8751 - 8765
Hlavní autoři: Zhang, Shuanghui, Liu, Yongxiang, Li, Xiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Alternating direction method of multipliers (ADMM)
Apertures
autofocusing
Computational efficiency
Computer applications
Convex functions
Echoes
Entropy
Image reconstruction
Imaging
Imaging techniques
Inverse problems
Inverse synthetic aperture radar
inverse synthetic aperture radar (ISAR)
Iterative methods
Mathematical analysis
Matrix methods
minimum entropy
Optimization
Phase error
Radar
Radar echoes
Radar imaging
SAR (radar)
Signal reconstruction
sparse aperture
Synthetic aperture radar
ISSN:0196-2892, 1558-0644
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Popis
Shrnutí:In the case of sparse aperture, the coherence between pulses of radar echo is destroyed, which challenges inverse synthetic aperture radar (ISAR) autofocusing and imaging. Mathematically, reconstructing the ISAR image from the sparse aperture radar echo is a linear underdetermined inverse problem, which, by nature, can be solved by the fast developed compressive sensing (CS) or sparse signal recovery theory. However, the CS-based sparse aperture ISAR imaging algorithms are generally computationally heavy, which becomes the bottleneck of preventing their applications to the real-time ISAR imaging system. In this article, we propose a novel and computationally efficient ISAR autofocusing and imaging algorithm for sparse aperture. We first consider a generalized CS model for ISAR imaging and autofocusing with sparse and entropy-minimization regularizations, and then utilize the alternating direction method of multipliers (ADMM) algorithm to optimize the model. To improve computational efficiency, the matrix inversion is translated to an elementwise division with the usage of a partial Fourier dictionary, and the 2-D ISAR image is updated as a whole instead of range cellwise. To achieve autofocusing for sparse aperture, the phase error is estimated by minimizing the entropy of the ISAR image reconstructed in each iterative loop. Experiments based on both simulated and measured data validate that the proposed algorithm can achieve well-focused ISAR images within a few seconds, which is ten times faster than the reported sparse aperture ISAR imaging algorithms.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.2990445