Compressive Sensing With Chaotic Sequence
Compressive Sensing With Chaotic Sequence
复制标题
混沌序列压缩感知
DOI：
10.1109/lsp.2010.2052243
复制
发表时间：
2010-08-01
影响因子：
3.9
通讯作者：
Sun, Hong
中科院分区：
文献类型：
--
作者：
Yu, Lei;Barbot, Jean Pierre;Sun, Hong
压缩感知是一种以亚奈奎斯特速率捕获信号的新方法。为了保证从压缩测量中精确恢复，应该选择满足限制等距属性（RIP）的特定矩阵来实现感测过程。本文提出了一种利用混沌序列构造感知矩阵的简单方法，并证明了这种矩阵以压倒性的概率保证RIP。与高斯随机矩阵、伯努利随机矩阵和稀疏矩阵进行了实验比较，结果表明，这些传感矩阵的性能基本相当。
Compressive sensing is a new methodology to capture signals at sub-Nyquist rate. To guarantee exact recovery from compressed measurements, one should choose specific matrix, which satisfies the Restricted Isometry Property (RIP), to implement the sensing procedure. In this letter, we propose to construct the sensing matrix with chaotic sequence following a trivial method and prove that with overwhelming probability, the RIP of this kind of matrix is guaranteed. Meanwhile, its experimental comparisons with Gaussian random matrix, Bernoulli random matrix and sparse matrix are carried out and show that the performances among these sensing matrix are almost equal.