A Method of Constructing Measurement Matrix for Compressed Sensing by Chebyshev Chaotic Sequence.
A Method of Constructing Measurement Matrix for Compressed Sensing by Chebyshev Chaotic Sequence.
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DOI：
10.3390/e22101085
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发表时间：
2020-09-26
期刊：
影响因子：
--
通讯作者：
Wu B
中科院分区：
文献类型：
--
作者：
Yi R;Cui C;Miao Y;Wu B
本文研究了压缩感知中测量矩阵的构造问题。在压缩感知中，构造一个性能良好且易于硬件实现的测量矩阵是一个很有意义的问题。最近的研究表明，由Logistic或Tent混沌序列构造的测量矩阵以一定的概率满足限制等距性（RIP），并且易于在物理电路中实现。然而，需要大的样本距离，这意味着大量的资源消耗，以获得不相关的样本从这些序列中的建设。针对这一问题，提出了一种利用Chebyshev混沌序列构造测量矩阵的方法。该方法有效地减小了样本距离，并在假设样本元素统计独立的前提下，证明了所提出的测量矩阵以很高的概率满足RIP。仿真结果表明，所提出的测量矩阵具有可比的压缩感知现有的混沌矩阵的重建性能。
In this paper, the problem of constructing the measurement matrix in compressed sensing is addressed. In compressed sensing, constructing a measurement matrix of good performance and easy hardware implementation is of interest. It has been recently shown that the measurement matrices constructed by Logistic or Tent chaotic sequences satisfy the restricted isometric property (RIP) with a certain probability and are easy to be implemented in the physical electric circuit. However, a large sample distance that means large resources consumption is required to obtain uncorrelated samples from these sequences in the construction. To solve this problem, we propose a method of constructing the measurement matrix by the Chebyshev chaotic sequence. The method effectively reduces the sample distance and the proposed measurement matrix is proved to satisfy the RIP with high probability on the assumption that the sampled elements are statistically independent. Simulation results show that the proposed measurement matrix has comparable reconstruction performance to that of the existing chaotic matrices for compressed sensing.
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影响因子：
1.1
作者：
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通讯作者：
Achlioptas, D
DOI：
10.1016/j.cnsns.2018.02.039
发表时间：
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影响因子：
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