An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm

Based on the sparsity of inverse synthetic aperture radar (ISAR) signal, in this paper, a novel high resolution imaging algorithm is proposed. In this method, an optimal ISAR signal model based on mixed norm is established by using compressed sensing theory. The high-resolution ISAR image with short...

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Main Authors: D. Song, Q. Chen, K. Li
Format: Article
Language:English
Published: Spolecnost pro radioelektronicke inzenyrstvi 2022-12-01
Series:Radioengineering
Subjects:
Online Access:https://www.radioeng.cz/fulltexts/2022/22_04_0477_0485.pdf
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author D. Song
Q. Chen
K. Li
author_facet D. Song
Q. Chen
K. Li
author_sort D. Song
collection DOAJ
description Based on the sparsity of inverse synthetic aperture radar (ISAR) signal, in this paper, a novel high resolution imaging algorithm is proposed. In this method, an optimal ISAR signal model based on mixed norm is established by using compressed sensing theory. The high-resolution ISAR image with short coherent accumulation time is realized by solving the optimization model. The main advantages of the proposed approach are: The model makes use of the l2,0 mixed norm to realize faster convergence and improve the computational speed of the model solution obviously. Moreover, according to the result sparsity of each iteration under arbitrary noise, the regularization coefficient in the model can be adjusted adaptively, which avoids the complex process of repeated attempts, otherwise, the optimal coefficient needs to be estimated and attempted by the statistical characteristics of the noise and signal. The effectiveness of the proposed method is verified by simulated and measured data.
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spelling doaj.art-2d56fbbd2d9d45faa8509a4f29a85fe82022-12-22T03:45:26ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122022-12-01314477485An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed NormD. SongQ. ChenK. LiBased on the sparsity of inverse synthetic aperture radar (ISAR) signal, in this paper, a novel high resolution imaging algorithm is proposed. In this method, an optimal ISAR signal model based on mixed norm is established by using compressed sensing theory. The high-resolution ISAR image with short coherent accumulation time is realized by solving the optimization model. The main advantages of the proposed approach are: The model makes use of the l2,0 mixed norm to realize faster convergence and improve the computational speed of the model solution obviously. Moreover, according to the result sparsity of each iteration under arbitrary noise, the regularization coefficient in the model can be adjusted adaptively, which avoids the complex process of repeated attempts, otherwise, the optimal coefficient needs to be estimated and attempted by the statistical characteristics of the noise and signal. The effectiveness of the proposed method is verified by simulated and measured data.https://www.radioeng.cz/fulltexts/2022/22_04_0477_0485.pdfinverse synthetic aperture radar (isar)mixed normregularization coefficientsparse constraint
spellingShingle D. Song
Q. Chen
K. Li
An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm
Radioengineering
inverse synthetic aperture radar (isar)
mixed norm
regularization coefficient
sparse constraint
title An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm
title_full An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm
title_fullStr An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm
title_full_unstemmed An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm
title_short An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm
title_sort adaptive sparse constraint isar high resolution imaging algorithm based on mixed norm
topic inverse synthetic aperture radar (isar)
mixed norm
regularization coefficient
sparse constraint
url https://www.radioeng.cz/fulltexts/2022/22_04_0477_0485.pdf
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