2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals...

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Published in:	Remote sensing (Basel, Switzerland) Vol. 9; no. 6; p. 619
Main Authors:	Li, Gongxin, Yang, Jia, Yang, Wenguang, Wang, Yuechao, Wang, Wenxue, Liu, Lianqing
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.06.2017
Subjects:	Algorithms Complexity Computation Computer applications Data processing Experiments Image reconstruction International conferences Iterative methods Radar Radar imaging Radar systems Recovery Remote sensing Sampling Signal processing Signal reconstruction Space surveillance Surveillance Theory Time compression Two dimensional models China
ISSN:	2072-4292, 2072-4292
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Abstract	Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery.
AbstractList	Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery.
Author	Yang, Wenguang Liu, Lianqing Li, Gongxin Yang, Jia Wang, Yuechao Wang, Wenxue
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SubjectTerms	Algorithms Complexity Computation Computer applications Data processing Experiments Image reconstruction International conferences Iterative methods Radar Radar imaging Radar systems Recovery Remote sensing Sampling Signal processing Signal reconstruction Space surveillance Surveillance Theory Time compression Two dimensional models
Title	2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging
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