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Sensors 2017, 17(6), 1353; doi:10.3390/s17061353

A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging

University of Electronic Science and Technology of China, Chengdu 610051
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Academic Editor: Mehrez Zribi
Received: 2 April 2017 / Revised: 6 June 2017 / Accepted: 7 June 2017 / Published: 10 June 2017
(This article belongs to the Section Remote Sensors)
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Abstract

This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model. Then, based on the Bayesian criterion, the widely-used sparse regularization is considered as the penalty term to recover the target distribution. The derivation of the cost function is described, and finally, an iterative expression for minimizing this function is presented. Alternatively, this paper discusses how to estimate the single parameter of Gaussian noise. With the advantage of a more accurate model, the proposed sparse Bayesian approach enjoys a lower model error. Meanwhile, when compared with the conventional superresolution methods, the proposed approach shows high cross-range resolution and small location error. The superresolution results for the simulated point target, scene data, and real measured data are presented to demonstrate the superior performance of the proposed approach. View Full-Text
Keywords: forward-looking imaging; Bayesian criterion; sparse regularization forward-looking imaging; Bayesian criterion; sparse regularization
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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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Zhang, Y.; Zhang, Y.; Huang, Y.; Yang, J. A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging. Sensors 2017, 17, 1353.

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