超稀疏三元循环测量矩阵的设计

发布时间：2018-06-11 11:39

  本文选题：香农采样定理 + 奈奎斯特率　； 参考：《华中科技大学学报(自然科学版)》2014年10期

【摘要】：在伯努利循环矩阵的基础上,对其独立元素中随机地引入零元,形成超稀疏三元循环矩阵,与伯努利-循环矩阵相比,其随机独立变元个数和矩阵非零元数目显著减少,从而有利于信息的传输和存储.数值实验结果表明:提出的测量矩阵重建效果略优于伯努利矩阵和伯努利循环矩阵的重建效果,并在绝大多数情形下重建时间可以降低到原来的10%~40%,加快了后端信号重建的速度,有利于压缩感知理论的实用化.
[Abstract]:On the basis of Bernoulli cyclic matrix, zero element is randomly introduced into its independent element to form super-sparse ternary cyclic matrix. Compared with Bernoulli cyclic matrix, the number of random independent variables and the number of nonzero elements of matrix are significantly reduced. This is beneficial to the transmission and storage of information. The numerical results show that the reconstruction effect of the proposed measurement matrix is slightly better than that of the Bernoulli matrix and the Bernoulli cyclic matrix, and the reconstruction time can be reduced to 100.40% of the original reconstruction time in most cases, which accelerates the speed of the back-end signal reconstruction. It is beneficial to the practical application of the theory of compressed perception.
【作者单位】： 安徽大学计算智能与信号处理教育部重点实验室;
【基金】：NSFC-广东联合基金资助项目(U1201255) 国家自然科学基金资助项目(61201396,61301296,61377006) 安徽大学博士科研启动经费资助项目(33190218)
【分类号】：TN911.7
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本文编号：2005069