Super-Resolution ISAR Imaging via Cosparse Model

HOU Biao; LI Zhengwei; ZHANG Guang; JIAO Licheng

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Super-Resolution ISAR Imaging via Cosparse Model

HOU Biao *,LI Zhengwei,ZHANG Guang,JIAO Licheng

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, 710071, China

*Correspondence author

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Funding: National Natural Science Foundation of China（No.61671350, 61173090, 61072106 and 61271302）, National Basic Research Program (973 Program) of China under Grant（No.2013CB329402）, National Research Foundation for the Doctoral Program of Higher Education of China（No.20130203110009）

Opened online: 9 May 2017

Accepted by: none

Citation: HOU Biao,LI Zhengwei,ZHANG Guang.Super-Resolution ISAR Imaging via Cosparse Model[OL]. [ 9 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4731585

A super-resolution inverse synthetic aperture radar (ISAR) imaging based on cosparse is proposed in this paper. Different from traditional imaging model, we regard the super-resolution imaging process as an analysis model. In order to obtain well-focused and denoised ISAR image, the phase adjustment is realized by analysis operator learning (AOL), and we add a new regularization item and use Augmented Lagrangian (AL) method to approximate the denoised signal. Then we use a modified OMP algorithm to recover the strong scattering coefficients, which can produce a well-focused image. This process can be seen as a multilayer imaging model and the quality of the imaging can be improved step by step. The experimental results show that the proposed method can get higher quality ISAR image than the traditional super-resolution imaging algorithms and is an effective approach to ISAR imaging within a short CPI.

Keywords:Inverse synthetic aperture radar (ISAR); Cosparse; Analysis model; Analysis operator learning (AOL); Coherent processing interval (CPI)

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