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Remote Sens. 2017, 9(6), 619; doi:10.3390/rs9060619

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

1
State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China
2
University of the Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Francesco Soldovieri, Raffaele Persico, Xiaofeng Li and Prasad S. Thenkabail
Received: 18 March 2017 / Revised: 9 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
View Full-Text   |   Download PDF [2104 KB, uploaded 19 June 2017]   |  

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. View Full-Text
Keywords: fast compressive radar imaging; compressive sensing; two dimensional normalized iterative hard thresholding (2D-NIHT) algorithm; compressive radar imaging model; reconstruction performance fast compressive radar imaging; compressive sensing; two dimensional normalized iterative hard thresholding (2D-NIHT) algorithm; compressive radar imaging model; reconstruction performance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, G.; Yang, J.; Yang, W.; Wang, Y.; Wang, W.; Liu, L. 2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging. Remote Sens. 2017, 9, 619.

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