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Sensors 2015, 15(3), 6924-6946; doi:10.3390/s150306924

Bayesian Deconvolution for Angular Super-Resolution in Forward-Looking Scanning Radar

School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Road, Gaoxin Western District, Chengdu 611731, China
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Author to whom correspondence should be addressed.
Academic Editor: James Churnside
Received: 16 January 2015 / Revised: 17 March 2015 / Accepted: 17 March 2015 / Published: 23 March 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [7664 KB, uploaded 23 March 2015]   |  

Abstract

Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning radar based on Bayesian theory, which states that the angular super-resolution can be realized by solving the corresponding deconvolution problem with the maximum a posteriori (MAP) criterion. The algorithm considers that the noise is composed of two mutually independent parts, i.e., a Gaussian signal-independent component and a Poisson signal-dependent component. In addition, the Laplace distribution is used to represent the prior information about the targets under the assumption that the radar image of interest can be represented by the dominant scatters in the scene. Experimental results demonstrate that the proposed deconvolution algorithm has higher precision for angular super-resolution compared with the conventional algorithms, such as the Tikhonov regularization algorithm, the Wiener filter and the Richardson–Lucy algorithm. View Full-Text
Keywords: deconvolution; Bayesian; radar imaging; super-resolution; convex optimization deconvolution; Bayesian; radar imaging; super-resolution; convex optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zha, Y.; Huang, Y.; Sun, Z.; Wang, Y.; Yang, J. Bayesian Deconvolution for Angular Super-Resolution in Forward-Looking Scanning Radar. Sensors 2015, 15, 6924-6946.

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