计算机与现代化 ›› 2010, Vol. 1 ›› Issue (10): 142-146.doi: 10.3969/j.issn.1006-2475.2010.10.038

• 网络与通信 • 上一篇    下一篇

稀疏信号重构

刘洪江   

  1. 阿坝师范高等专科学校计算机科学系,四川 成都 611741
  • 收稿日期:2010-06-10 修回日期:1900-01-01 出版日期:2010-10-21 发布日期:2010-10-21

Reconstruction of Sparse Signal

LIU Hong-jiang   

  1. Department of Computer Science, Aba Teachers College, Chengdu 611741, China
  • Received:2010-06-10 Revised:1900-01-01 Online:2010-10-21 Published:2010-10-21

摘要: 传统采样是在预先确定好采样时间的情况下记录信号的电平,这样采样得到的电平通常是均匀的,另外还有一种隐式采样模型,原理是记录事先确定好的电平交叉点的时序,这种情况下信号决定了采样的次数而不是电平。在信号存在一个尺度因子的前提下,Logan理论是零交叉点信号还原的充分条件。不过时序测量存在一定的噪声,故信号的重构不具鲁棒性。为此本文引入附加假设,即信号存在一些稀疏基,因而可以把重构问题当成对诱导稀疏性成本函数的最小化来处理,并提供一个求解算法。尽管存在非凸的问题,实验表明在典型案例中算法是收敛的,并且求出正确解的概率很高。

关键词: 电平交叉, 信号重构, 采样

Abstract: The traditional sampling method records the level of signal at the pre-determined time. Therefore the levels sampled are often uniformed. There is another sampling model-implicit sampling, it records the timing of level-crossings which are determined before sampling. Under this situation, the signal determines the times but not level of sampling. In the condition that one scalar factor exists, Logan theorem provides enough conditions for signal reconstruction from zero crossings. Since there are some noises in the measurement of timing, the reconstruction usually is not robust. So this paper introduces additional condition: there are some sparse bases in the signal so that it makes reconstruction robust. Under this assumption, it can regard the reconstruction as minimization of sparsity inducing cost function and provide an algorithm. Although the problem is not convex, the experiments show in the classic cases the algorithm is converged and gives a correct solution with high probability.

Key words: level crossing, signal reconstruction, sampling

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