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

Sensors

Yuebo Zha¹  Yulin Huang²  Zhichao Sun²  Yue Wang²  Jianyu Yang² 

[1] School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Road, Gaoxin Western District, Chengdu 611731, China;

关键词: deconvolution;    Bayesian;    radar imaging;    super-resolution;    convex optimization;   

DOI  :  10.3390/s150306924

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.