Superresolution of Radar Forward-Looking Imaging Based on Accelerated TV-Sparse Method

Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging. However, due to the existence of matrix inversion, it suffers from high computationa...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 92 - 102
Main Authors: Zhang, Yin, Zhang, Qiping, Zhang, Yongchao, Huang, Yulin, Yang, Jianyu
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Azimuth
Complexity
Computer applications
Computing time
Data analysis
Data processing
Gohberg–Semencul (GS) representation
Image resolution
Imaging
Imaging techniques
Radar
Radar antennas
Radar imaging
Signal resolution
sparse
superresolution
Target recognition
total variation
ISSN:1939-1404, 2151-1535
Online Access:Get full text
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Summary:Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging. However, due to the existence of matrix inversion, it suffers from high computational complexity, which restricts the ability of radar real-time imaging. In this article, an Gohberg-Semencul (GS) decomposition-based fast TV-sparse (FTV-sparse) method is proposed to reduce the computational complexity of TV-sparse method. The acceleration strategy utilizes the low displacement rank features of Toeplitz matrix, realizing fast matrix inversion by using a GS representation. It reduces the computational complexity of traditional TV-sparse method from O(N 3 ) to O(N 2 ), benefiting for improvement of the computing efficiency. The simulation and experimental data processing results show that the proposed FTV-sparse method has almost no resolution loss compared with the traditional TV sparse method. Hardware test results show that the proposed FTV-sparse method significantly improves the computational efficiency of TVsparse method.
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content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.3033823