A sparse imaging algorithm of ISAR based on generalized minimax concave (GMC) penalty is deseribed in this paper. The penalty of the algorithm is different from that of the L1 norm regularization. The penalty function not only maintains the convexity of the least squares cost function to be minimized but also avoids the systematic underestimation characteristic of the L1 norm regularization. This work illustrates the amplitude preservation characteristics of GMC algorithm in ISAR imaging by simulation experiments and imaging results of real data of Yak-42 aircraft. The results show that GMC algorithm has obvious advantages in imaging accuracy and has better imaging effect.
YANG Li
,
WEI Zhonghao
,
ZHANG Bingchen
,
LU Xiaojun
. ISAR sparse imaging algorithm based on generalized minimax concave penalty[J]. Journal of University of Chinese Academy of Sciences, 2019
, 36(2)
: 244
-250
.
DOI: 10.7523/j.issn.2095-6134.2019.02.012
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