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Information 2015, 6(4), 650-668; doi:10.3390/info6040650

Bayesian Angular Superresolution Algorithm for Real-Aperture Imaging in Forward-Looking 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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Academic Editor: Willy Susilo
Received: 8 September 2015 / Revised: 9 October 2015 / Accepted: 9 October 2015 / Published: 15 October 2015
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Abstract

In real aperture imaging, the limited azimuth angular resolution seriously restricts the applications of this imaging system. This report presents a maximum a posteriori (MAP) approach based on the Bayesian framework for high angular resolution of real aperture radar. First, Rayleigh statistic and the lq norm (for 0 < q ≤ 1) sparse constraint are considered to express the clutter property and target scattering coefficient distribution, respectively. Then, the MAP objective function is established according to the hypotheses above. At last, a recursive iterative strategy is developed to estimate the original target scattering coefficient distribution and clutter statistic. The comparison of simulations and experimental results are given to verify the performance of our proposed algorithm. View Full-Text
Keywords: real aperture radar; angular superresolution; Bayesian framework; sparse constraint; maximum a posteriori real aperture radar; angular superresolution; Bayesian framework; sparse constraint; maximum a posteriori
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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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Zha, Y.; Zhang, Y.; Huang, Y.; Yang, J. Bayesian Angular Superresolution Algorithm for Real-Aperture Imaging in Forward-Looking Radar. Information 2015, 6, 650-668.

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