Bistatic inverse synthetic aperture radar sparse aperture self-focusing algorithm based on the joint constraint of compressed sensing and minimum Tsallias entropy

Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint...

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Published inJournal of applied remote sensing Vol. 16; no. 3; p. 036504
Main Authors Zhu, Hanshen, Guo, Baofeng, Hu, Wenhua, Jiao, Liting, Zhu, Xiaoxiu, Xue, Dongfang, Zhu, Chang’an
Format Journal Article
LanguageEnglish
Published Society of Photo-Optical Instrumentation Engineers 01.07.2022
Subjects
sparse aperture
fast sparse Bayesian learning
self-focusing
Bistatic inverse synthetic aperture radar
Tsallis entropy
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Abstract Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint of image quality optimization and sparsity is proposed. First, the proposed algorithm establishes the Bi-ISAR sparse aperture self-focusing signal model, reconstructs images through fast sparse Bayesian learning (FSBL), uses the minimum Tsallis entropy and constraints the reconstruction process, iteratively updates the phase error, and performs self-focusing to realize the initial phase correction of Bi-ISAR images. Simulation results show that the proposed algorithm has a fast convergence speed, strong robustness to noise, and high accuracy in reconstructing images.
AbstractList Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint of image quality optimization and sparsity is proposed. First, the proposed algorithm establishes the Bi-ISAR sparse aperture self-focusing signal model, reconstructs images through fast sparse Bayesian learning (FSBL), uses the minimum Tsallis entropy and constraints the reconstruction process, iteratively updates the phase error, and performs self-focusing to realize the initial phase correction of Bi-ISAR images. Simulation results show that the proposed algorithm has a fast convergence speed, strong robustness to noise, and high accuracy in reconstructing images.
Author Xue, Dongfang
Hu, Wenhua
Zhu, Hanshen
Zhu, Xiaoxiu
Guo, Baofeng
Jiao, Liting
Zhu, Chang’an
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Keywords sparse aperture
fast sparse Bayesian learning
self-focusing
Bistatic inverse synthetic aperture radar
Tsallis entropy
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Title Bistatic inverse synthetic aperture radar sparse aperture self-focusing algorithm based on the joint constraint of compressed sensing and minimum Tsallias entropy
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