西安电子科技大学学报

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低复杂度压缩感知中的快速观测方法

权磊;肖嵩;薛晓;李颖   

  1. (西安电子科技大学 通信工程学院,陕西 西安 710071)
  • 收稿日期:2016-01-22 出版日期:2017-02-20 发布日期:2017-04-01
  • 作者简介:权磊(1989-),男,西安电子科技大学博士研究生,E-mail: gloomy2110@hotmail.com
  • 基金资助:

    国家自然科学基金资助项目(61372069);“111”工程资助项目(B08038)

Fast sensing method in compressive sensing with low complexity

QUAN Lei;XIAO Song;XUE Xiao;LI Ying   

  1. (School of Telecommunications Engineering, Xidian Univ., Xi'an 710071, China)
  • Received:2016-01-22 Online:2017-02-20 Published:2017-04-01

摘要:

为了解决压缩感知采样系统中随机观测算法硬件实现复杂而确定性观测算法难以实现大信号高效测量的问题,提出了一种用于低复杂度压缩感知的快速观测方法.设计了m序列交织器对输入信号进行白化处理,然后对白化信号进行快速哈达玛变换并下采样得到测量样本.理论分析表明,该算法对应的传感矩阵满足准高斯特性.仿真结果表明,该算法对应的观测矩阵具有和随机观测矩阵几乎相同的性能以及更短的观测时间.该算法具有良好的测量性能并易于硬件实现,具有一定的实用价值.

关键词: 压缩感知, 快速观测, 低复杂度, m序列

Abstract:

The random sensing algorithms are hard for hardware implementation while the deterministic sensing algorithms have difficulty in acquiring large signals in the sensing systems of compressive sensing. This paper proposes a fast sensing method for compressive sensing with low complexity. The input signal is firstly permuted by an m-sequence controlled interleaving device. Then the permuted signal is transformed by the fast Walsh-Hadamard transform and down sampled to generate the measurements. Theoretical analysis indicates that the entries of the corresponding sensing matrices are asymptotically normally distributed. Simulation results show that the sensing performance of the corresponding matrices is almost the same as that of completely random sensing operators with a shorter computational time cost. The proposed method has good sensing performance and is easier for hardware implementation, which is meaningful in practice.

Key words: compressive sensing, fast sensing, low-complexity, m-sequence