期刊文献+

一种构造压缩感知测量矩阵的新方法 被引量:2

A new method to construct measurement matrix based on compressed sensing
下载PDF
导出
摘要 压缩感知理论是近年来针对稀疏信号提出的一种新的信号处理理论。该理论的主要创新之处在于对信号采样和压缩是同时进行的。测量矩阵是实现该创新点的关键步骤之一,其性能直接关系着信号能不能精确重构。利用行列式非零的对角矩阵的正交性,结合正交基线性表示理论,提出了一种新的更简单的测量矩阵的构造方法。通过实验仿真,验证了新矩阵具有较好的性能。 Compressed sensing theory which is proposed recently for sparse signal is a new theory used in signal processing area. It's main innovation is to sample and compress signal at the same time. Measurement matrix is one significant step to implement the innovation, and it's property has a direct effect on the result of signal reconstruction. This essay proposed a new simple method to construct the measurement matrix , utilizing the orthogonality of diagonal matrix with its determinant of non-zero value, and linear representation theory of orthogonal basis. Simulation result proves that the new matrix has a good performance.
出处 《微型机与应用》 2014年第4期74-76,共3页 Microcomputer & Its Applications
关键词 压缩感知 测量矩阵 对角阵 正交基线性表示 compressed sensing measurement matrix diagonal matrix linear representation theory of orthogonal basis
  • 相关文献

参考文献10

  • 1吴海佳,张雄伟,陈卫卫.压缩感知新技术专题讲座(二) 第4讲 压缩感知理论中测量矩阵的构造方法[J].军事通信技术,2012,33(1):90-94. 被引量:4
  • 2CANDES E, TAO T.Near optimal signal recovery from random projections : universal encoding strategies[J].IEEE Trans. Inform.Theory, 2006,52 (12) : 5406-5425.
  • 3DONOHO D.Compressed sensing[J].IEEE Trans. Inform. Theory, 2006,52(4) : 1289-1306.
  • 4CANDIES E ,WAKIN M.An introduction to compressive sampling[J].IEEE Signal Processing Magazine,2008(25): 21-30.
  • 5DONOHO D ,TSAIG Y.Extensions of compressed sensing[J]. Signal Processing, 2006,86(3) : 533-548.
  • 6CANDES E.The restricted isometry property and its implications for compressed sensing[J].C.R.Math.Acad.Sci, 2008,346(9-10) : 589-592.
  • 7DEVORE V.Deterministic constructions of compressed sensing matrices[J].Journal of Complexity, 2007,23(4-6) : 918-925.
  • 8RAUHUT H.Circulant and toeplitz matrices in compressed sensing[J].In Processing SPARS'09,2009,2(13) : 1124- 1132.
  • 9李树涛,魏丹.压缩传感综述[J].自动化学报,2009,35(11):1369-1377. 被引量:205
  • 10TROPP J, GILBERT A C.Signal recovery from partial information via orthogonal matching pursuit[J].IEEE Transactions on Information Theory, 2007,53(12) : 4655-4666.

二级参考文献61

  • 1Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
  • 2Candes E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
  • 3Candes E. Compressive sampling. In: Proceedings of International Congress of Mathematicians. Madrid, Spain: European Mathematical Society Publishing House, 2006. 1433-1452.
  • 4Baraniuk R G. Compressive sensing. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.
  • 5Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 1996, 381(6583): 607-609.
  • 6Mallat S. A Wavelet Tour of Signal Processing. San Diego: Academic Press, 1996.
  • 7Candes E, Donoho D L. Curvelets - A Surprisingly Effective Nonadaptive Representation for Objects with Edges, Technical Report 1999-28, Department of Statistics, Stanford University, USA, 1999.
  • 8Aharon M, Elad M, Bruckstein A M. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Transactions on Image Processing, 2006, 54(11): 4311-4322.
  • 9Rauhut H, Schnass K, Vandergheynst P. Compressed sensing and redundant dictionaries. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219.
  • 10Candes E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problems, 2007, 23(3): 969-985.

共引文献207

同被引文献19

引证文献2

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部