Open Access
7 January 2015 Two-dimensional sparse synthetic aperture radar imaging method with stepped-frequency waveform
Fufei Gu, Qun Zhang, Hao Lou, Zhi’an Li, Ying Luo
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Abstract
Stepped-frequency waveforms (SFWs) can use the digital signal processing method to obtain high-range resolution with relatively narrow instantaneous bandwidth, which has been used in synthetic aperture radar (SAR). However, SFWs have the disadvantages of poor antijamming capability and a long period of transmission. Also, in the coherent integration time, some echo data are frequently lost. A two-dimensional sparse imaging method in the space and frequency domains for SAR is proposed based on compressed sensing (CS) theory. A sparse SFW for SAR imaging is formed and analyzed first, which has the advantages of better antijamming capability and a shorter time period of transmission. The range compression is completed by using CS theory. As to the sparse echo data in the space domain, the imaging operator and the CS-based imaging scheme are constructed to simultaneously implement the range cell migration correction and azimuth compression. Compared with the conventional SAR imaging method of SFWs, a much smaller number of frequencies and a smaller amount of imaging data are required for SAR imaging by using the proposed method. Finally, the effectiveness of the proposed method is proven by simulation and experimental results.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Fufei Gu, Qun Zhang, Hao Lou, Zhi’an Li, and Ying Luo "Two-dimensional sparse synthetic aperture radar imaging method with stepped-frequency waveform," Journal of Applied Remote Sensing 9(1), 096099 (7 January 2015). https://doi.org/10.1117/1.JRS.9.096099
Published: 7 January 2015
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
Synthetic aperture radar

Radar imaging

Radar

Signal to noise ratio

Signal processing

Image quality

Compressed sensing

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