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动态压缩感知综述

荆楠,  毕卫红,  胡正平,  王林

文章导航 > 自动化学报  > 2015 >  41(1): 22-37
苏善伟, 朱波, 向锦武, 林岩. 非线性非最小相位系统的控制研究综述. 自动化学报, 2015, 41(1): 9-21. doi: 10.16383/j.aas.2015.c140322
引用本文: 荆楠, 毕卫红, 胡正平, 王林. 动态压缩感知综述. 自动化学报, 2015, 41(1): 22-37. doi: 10.16383/j.aas.2015.c140087
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SU Shan-Wei, ZHU Bo, XIANG Jin-Wu, LIN Yan. A Survey on the Control of Nonlinear Non-minimum Phase Systems. ACTA AUTOMATICA SINICA, 2015, 41(1): 9-21. doi: 10.16383/j.aas.2015.c140322
Citation: JING Nan, BI Wei-Hong, HU Zheng-Ping, WANG Lin. A Survey on Dynamic Compressed Sensing. ACTA AUTOMATICA SINICA, 2015, 41(1): 22-37. doi: 10.16383/j.aas.2015.c140087
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动态压缩感知综述

doi: 10.16383/j.aas.2015.c140087
  • 1.

    燕山大学信息科学与工程学院 秦皇岛 066004

基金项目: 

国家自然科学基金(61303233, 61201263, 61102110);河北省高等学校科学技术研究青年基金(QN20131058);河北省自然科学基金(F201 4203062)资助

详细信息
    作者简介:

    荆楠 燕山大学信息科学与工程学院副教授.2015年获燕山大学电路与系统博士学位.主要研究方向为时变稀疏信号估计及其应用,MIMO-OFDM系统信道估计.E-mail:jingnan@ysu.edu.cn

    通讯作者:

    毕卫红 燕山大学信息科学与工程学院教授.2003年获哈尔滨工业大学仪器科学与技术博士学位.主要研究方向为光电传感,光通信和无线传感器网络.本文通信作者. E-mail:jingenna@hotmail.com

A Survey on Dynamic Compressed Sensing

  • 1.

    School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004

Funds: 

Supported by National Natural Science Foundation of China (61303233, 61201263, 61102110), Natural Science Research Programs of Hebei Educational Committee for University Young Teachers (QN20131058), and National Natural Science Foundation of Hebei (F2014203062)

摘要

    摘要
  • 摘要: 动态压缩感(Dynamic compressed sensing, DCS)知由视频信号处理问题引出, 是压缩感知(Compressed sensing, CS)理论研究领域中新兴起的一个研究分支, 旨在处理信号支撑集随时间发生变化的时变稀疏信号, 较为成功的应用范例是动态核磁共振成像. 本文首先介绍动态系统模型, 给出时变稀疏信号支撑集缓慢变化的定义、 时变稀疏信号的稀疏表示和感知测量的方法; 其次, 建立一个统一的时变稀疏信号重构模型, 基于该模型对现有算法进行分类, 简要综述时变稀疏信号的重构算法, 并且对比分析算法的性能; 最后, 讨论动态压缩感知的应用, 并对其研究前景进行展望.
    Abstract: In video signal processing, dynamic compressed sensing (DCS) is a novel branch of compressed sensing (CS) theory for recovery of compressible, possibly with a slowly varying sparsity pattern, signal from a time sequence of noisy observations. Dynamic compressed sensing has been employed in dynamic magnetic resonance image reconstruction successfully. The system model for dynamic compressed sensing is first introduced, including definitions of slowly varying supports, sparse representations and stable measurement of the time-varying sparse signal. Then, a unified framework is formulated for reconstruction of the time-varying sparse signal. Based on the framework, classification is conducted for the existing algorithms whose main ideas, reconstruction procedures and performance are also commented briefly. Finally, the applications and future directions of dynamic compressed sensing are pointed out.
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  • 收稿日期:  2014-02-14
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