Zha Yuebo, Huang Yulin, Sun Zhichao, Wang Yue, Yang Jianyu
School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Road, Gaoxin Western District, Chengdu 611731, China.
Sensors (Basel). 2015 Mar 23;15(3):6924-46. doi: 10.3390/s150306924.
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.
扫描雷达对于地面监视、地形测绘和灾难救援具有显著重要性。然而，与可实现的距离分辨率相比，扫描雷达图像的角分辨率较差。本文提出了一种基于贝叶斯理论的扫描雷达角超分辨率反卷积算法，该理论指出通过使用最大后验（MAP）准则解决相应的反卷积问题可实现角超分辨率。该算法认为噪声由两个相互独立的部分组成，即高斯信号无关分量和泊松信号相关分量。此外，在假设感兴趣的雷达图像可由场景中的主要散射体表示的情况下，使用拉普拉斯分布来表示关于目标的先验信息。实验结果表明，与传统算法（如蒂霍诺夫正则化算法、维纳滤波器和理查森 - Lucy算法）相比，所提出的反卷积算法在角超分辨率方面具有更高的精度。
