TY - JOUR
T1 - Near range radar imaging based on block sparsity and cross-correlation fusion algorithm
AU - Feng, Weike
AU - Yi, Li
AU - Sato, Motoyuki
N1 - Funding Information:
Manuscript received September 13, 2017; revised November 20, 2017; accepted January 9, 2018. Date of publication June 7, 2018; date of current version June 29, 2018. This work was supported by JSPS Grant-in-Aid for Scientific Research (A) under Grant 26249058. (Corresponding author: Weike Feng.) W. Feng is with the Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan (e-mail; feng.weike.q4@dc.tohoku.ac.jp).
Publisher Copyright:
© 2008-2012 IEEE.
PY - 2018/6
Y1 - 2018/6
N2 - In this paper, we propose a novel processing model for compressive sensing (CS)-based stepped-frequency continuous-wave (SFCW) radar near range imaging, which takes the azimuth dependence of the reflection coefficients of targets into consideration. Based on the block sparse property of the received signal in the defined dictionary, 2-D images of the targets can be obtained at each spatial sampling point. A cross-correlation method is then employed to fuse these 2-D images to obtain the final result. Random undersamplings of frequencies and spatial sampling points are conducted to reduce the data acquisition time, the data size, and the computational complexity. Experimental results of an SFCW-based MIMO radar and a ground-based SAR system show that, compared with the conventional matched filtering-based methods, the proposed method can provide artifacts-reduced higher resolution images by using reduced frequencies and spatial sampling points. We also demonstrate that, compared to the conventional CS-based methods, due to the more suitable established observation model, the proposed method can achieve better imaging results with fewer artifacts for near range targets.
AB - In this paper, we propose a novel processing model for compressive sensing (CS)-based stepped-frequency continuous-wave (SFCW) radar near range imaging, which takes the azimuth dependence of the reflection coefficients of targets into consideration. Based on the block sparse property of the received signal in the defined dictionary, 2-D images of the targets can be obtained at each spatial sampling point. A cross-correlation method is then employed to fuse these 2-D images to obtain the final result. Random undersamplings of frequencies and spatial sampling points are conducted to reduce the data acquisition time, the data size, and the computational complexity. Experimental results of an SFCW-based MIMO radar and a ground-based SAR system show that, compared with the conventional matched filtering-based methods, the proposed method can provide artifacts-reduced higher resolution images by using reduced frequencies and spatial sampling points. We also demonstrate that, compared to the conventional CS-based methods, due to the more suitable established observation model, the proposed method can achieve better imaging results with fewer artifacts for near range targets.
KW - Block sparsity
KW - compressive sensing (CS)
KW - cross correlation
KW - stepped-frequency continuous wave (SFCW)
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U2 - 10.1109/JSTARS.2018.2797056
DO - 10.1109/JSTARS.2018.2797056
M3 - Article
AN - SCOPUS:85048507757
VL - 11
SP - 2079
EP - 2089
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
SN - 1939-1404
IS - 6
ER -