Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80451
Title: Logarithmic laplacian prior based bayesian inverse synthetic aperture radar imaging
Authors: Zhang, Shuanghui
Liu, Yongxiang
Li, Xiang
Bi, Guoan
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Sparse Signal Recovery
Inverse Synthetic Aperture Radar Imaging (ISAR)
Issue Date: 2016
Source: Zhang, S., Liu, Y., Li, X., & Bi, G. (2016). Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging. Sensors, 16(5), 611-. doi:10.3390/s16050611
Series/Report no.: Sensors
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.
URI: https://hdl.handle.net/10356/80451
http://hdl.handle.net/10220/46535
ISSN: 1424-8220
DOI: 10.3390/s16050611
Schools: School of Electrical and Electronic Engineering 
Rights: © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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