压缩感知综述  

Survey of compressed sensing

作　　者：尹宏鹏[1] 刘兆栋[1] 柴毅[1,2] 焦绪国[1] 

机构地区：[1]重庆大学自动化学院,重庆400044 [2]重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆400044

出　　处：《控制与决策》2013年第10期1441-1445,1453,共6页Control and Decision

基　　金：国家自然科学基金项目(61203321);中国博士后基金项目(2012M521676);中央高校基金科研业务费项目(106112013CD-JZR170005)

摘　　要：压缩感知理论的诞生使得采样速率与信号的结构和内容相关,并以低于奈奎斯特采样定理要求的频率采样、编码和重构.在实际应用中,为解决数据冗余和资源浪费的瓶颈问题开拓了一条新道路,也为其他学科发展提供了新的契机.从发展历史和研究现状等方面入手,对稀疏表示、测量矩阵的构造、稀疏重构算法和主要应用方面进行了详细的梳理和研究.对当前研究的热点、难点作了分析和探讨,并指出了未来的发展方向和应用前景.The presence of compressed sensing theory makes the sample rate relate to the signal structure and content. The sample rate of compressed sensing for the signal sample, coding and reconstruction is less than the Nyquist theorem methods. Therefore, a method is proposed to solve the bottleneck problem of data redundancy and resource-wasting. Moreover, it offers new developing chances for other research fields. The development and current situation of compressed sensing theory are involved. A detailed carding and research on the sparse representation, measurement matrix design, reconstruction algorithm and application aspects is discussed. Therefore, the current hot spots and difficulties are analyzed and discussed. Finally, the direction of future development and application prospect are discussed.

关 键 词：压缩感知 稀疏表示 测量矩阵 稀疏重构算法 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]