Wang Tianyun, Lu Xinfei, Yu Xiaofei, Xi Zhendong, Chen Weidong
Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, University of Science and Technology of China, Hefei 230027, China.
China Satellite Maritime Tracking and Control Department, Jiangyin 214431, China.
Sensors (Basel). 2014 Mar 26;14(4):5929-51. doi: 10.3390/s140405929.
In recent years, various applications regarding sparse continuous signal recovery such as source localization, radar imaging, communication channel estimation, etc., have been addressed from the perspective of compressive sensing (CS) theory. However, there are two major defects that need to be tackled when considering any practical utilization. The first issue is off-grid problem caused by the basis mismatch between arbitrary located unknowns and the pre-specified dictionary, which would make conventional CS reconstruction methods degrade considerably. The second important issue is the urgent demand for low-complexity algorithms, especially when faced with the requirement of real-time implementation. In this paper, to deal with these two problems, we have presented three fast and accurate sparse reconstruction algorithms, termed as HR-DCD, Hlog-DCD and Hlp-DCD, which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularizations, by combining with the grid refinement technique. Experimental results are provided to demonstrate the effectiveness of the proposed algorithms and related analysis.
近年来，从压缩感知（CS）理论的角度出发，已经探讨了各种关于稀疏连续信号恢复的应用，如源定位、雷达成像、通信信道估计等。然而，在考虑任何实际应用时，有两个主要缺陷需要解决。第一个问题是由任意位置的未知量与预先指定的字典之间的基不匹配引起的离网格问题，这会使传统的CS重建方法性能大幅下降。第二个重要问题是对低复杂度算法的迫切需求，特别是当面临实时实现的要求时。在本文中，为了解决这两个问题，我们提出了三种快速且准确的稀疏重建算法，称为HR-DCD、Hlog-DCD和Hlp-DCD，它们基于同伦、二分坐标下降（DCD）迭代和非凸正则化，并结合了网格细化技术。提供了实验结果来证明所提出算法的有效性以及相关分析。
