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Journal of Applied Remote Sensing • Open Access

Augmented Lagrangian method for angular super-resolution imaging in forward-looking scanning radar
Author(s): Yuebo Zha; Yulin Huang; Jianyu Yang

Paper Abstract

Angular super-resolution imaging in the forward-looking area of a scanning radar platform plays an important role in the application of scanning radar. However, the angular resolution of scanning radar is limited by the system parameters. Thus, improving the angular resolution of scanning radar beyond the limitation of the given system parameters is desired. We present an angular super-resolution imaging method by solving the associated deconvolution problem. We first formulate an angular super-resolution problem in scanning radar as a deconvolution task and then convert it to a constrained optimization problem by incorporating the prior information of the target in the scene. We then solve the constrained optimization problem in convex optimization framework using an augmented Lagrangian method. In order to solve the constrained optimization problem, a corresponding augmented Lagrangian function is constructed and its saddle point is found using alternating direction method. The advantages of the proposed method for angular super-resolution imaging in scanning radar are that the proposed method can not only realize the angular super-resolution imaging in scanning radar but also has high precision. Simulation and experiment results are given at the end to verify the validity of the proposed method compared with a Wiener filter that is applicable for angular super-resolution in scanning radar.

Paper Details

Date Published: 7 May 2015
PDF: 15 pages
J. Appl. Remote Sens. 9(1) 096055 doi: 10.1117/1.JRS.9.096055
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Yuebo Zha, Univ. of Electronic Science and Technology of China (China)
Yulin Huang, Univ. of Electronic Science and Technology of China (China)
Jianyu Yang, Univ. of Electronic Science and Technology of China (China)


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