ISAR sparse imaging algorithm based on generalized minimax concave penalty

doi:10.7523/j.issn.2095-6134.2019.02.012

›› 2019, Vol. 36 ›› Issue (2): 244-250.DOI: 10.7523/j.issn.2095-6134.2019.02.012

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ISAR sparse imaging algorithm based on generalized minimax concave penalty

YANG Li^1,2, WEI Zhonghao^1,2, ZHANG Bingchen¹, LU Xiaojun³

1. Key Laboratory of Spatial Information Processing and Application System Technology of CAS, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. China International Engineering Consulting Corporation, Beijing 100048, China

Received:2018-01-08 Revised:2018-03-20 Online:2019-03-15

Abstract

Abstract: 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.

Key words: ISAR, generalized minimax concave penalty, L₁ norm regularization

CLC Number:

TN957

YANG Li, WEI Zhonghao, ZHANG Bingchen, LU Xiaojun. ISAR sparse imaging algorithm based on generalized minimax concave penalty[J]. , 2019, 36(2): 244-250.

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ISAR sparse imaging algorithm based on generalized minimax concave penalty

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