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Sensors (Basel). 2016 Apr 28;16(5). pii: E611. doi: 10.3390/s16050611.

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging.

Author information

1
School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. zhangshuanghui@nudt.edu.cn.
2
School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. lyx_bible@sina.com.
3
School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. lixiang01@vip.sina.com.
4
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. egbi@ntu.edu.sg.

Abstract

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

KEYWORDS:

autofocusing; inverse synthetic aperture radar imaging (ISAR); logarithmic Laplacian prior; maximum a posterior (MAP); quasi-Newton method; sparse signal recovery

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