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Construction of a compressive measurement matrix is one of the key technologies of compressive sensing. A circulant matrix corresponds to the discrete convolutions with a high-speed algorithm, which has been widely used in compressive sensing. This paper combines the advantages of chaotic sequence with circulant matrix to propose a circulant compressive measurement matrix based on the chaotic sequence. The elements of a chaotic circulant measurement matrix are generated by taking advantage of the chaotic internal certainty, i.e. the independent identically distributed randomness sequence can be produced by the chaotic mapping formula using the initial value and a certain sampling distance. At the same time, the external randomness of chaotic sequence can satisfy the stochastic requirements of compressive measurement matrix. This paper presents the method of constructing chaotic circulant measurement matrix using a Cat chaotic map and the test method for RIPless property of the matrix. Measurement results of one-dimensional and two-dimensional signals using the chaotic circulant measurement matrix are studied and are compared with the results of conventional circulant measurement matrix. It can be shown that the chaotic circulant measurement matrix has good recovery results for both one-dimensional and two-dimensional signals. Moreover, it may get better results than the traditional matrix for the two-dimensional signal. From the point of view of phase diagram, the essential reason of chaotic circulant measurement matrix outperforms the conventional one is its integration of internal certainty with the external randomness of the chaotic sequence.
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Keywords:
- compressive sensing /
- circulant matrix /
- chaotic sequence /
- RIPless theory
[1] Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289
[2] Candès E J, Romberg J, Tao T 2006 IEEE Trans. Inform. Theory 52 489
[3] Gribonval R, Nielsen M 2003 IEEE Trans. Inform. Theory 49 3320
[4] Candès E J, Wakin M B 2008 IEEE Sig. Proc. Mag. 25 21
[5] Candès E J 2008 Comptes. Rendus Math. 346 589
[6] Baraniuk R, Davenport M, DeVore R, Wakin M 2008 Constructive Approx. 23 253
[7] Candès E J, Plan Y 2011 IEEE Trans. Inform. Theory 57 7235
[8] Chen S S, Donoho D L, Saunders M A 2001 SIAM 43 129
[9] Needell D 2009 Topics in compressed sensing Ph. D. Dissertation (California: University of California)
[10] Zhang H M, Wang L Y, Yan B, Li L, Xi X Q, Lu L Z 2013 Chin. Phys. B 22 078701
[11] Feng B C, Fang S, Zhang L G, Li H, Tong J J, Li W Q 2013 Acta Phys. Sin. 62 112901(in Chinese) [冯丙辰, 方晟, 张立国, 李红, 童节娟, 李文茜 2013 物理学报 62 112901]
[12] Yin W, Morgan S, Yang J F, Zhang Y 2010 Visual Communications and Image Processing (International Society for Optics and Photonics) p77440K
[13] Romberg J 2009 SIAM. J. Imaging Sci. 2 1098
[14] Guo J B, Xu X Z, Shi Q H, Hu T H 2013 Acta Phys. Sin. 62 110508(in Chinese) [郭静波, 徐新智, 史启航, 胡铁华 2013 物理学报 62 110508]
[15] Yang J, Zhang Y 2011 SIAM. J. Sci. Comp. 33 250
[16] Chen G, Mao Y, Chui C K 2004 Chaos Solitons & Fractals 21 749
[17] Badea B, Vlad A 2006 Computational Science and Its Applications (Berlin: Springer Berlin Heidelberg) p1166
[18] Hoeffding W 1963 J. American Stat. Assoc. 58 13
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[1] Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289
[2] Candès E J, Romberg J, Tao T 2006 IEEE Trans. Inform. Theory 52 489
[3] Gribonval R, Nielsen M 2003 IEEE Trans. Inform. Theory 49 3320
[4] Candès E J, Wakin M B 2008 IEEE Sig. Proc. Mag. 25 21
[5] Candès E J 2008 Comptes. Rendus Math. 346 589
[6] Baraniuk R, Davenport M, DeVore R, Wakin M 2008 Constructive Approx. 23 253
[7] Candès E J, Plan Y 2011 IEEE Trans. Inform. Theory 57 7235
[8] Chen S S, Donoho D L, Saunders M A 2001 SIAM 43 129
[9] Needell D 2009 Topics in compressed sensing Ph. D. Dissertation (California: University of California)
[10] Zhang H M, Wang L Y, Yan B, Li L, Xi X Q, Lu L Z 2013 Chin. Phys. B 22 078701
[11] Feng B C, Fang S, Zhang L G, Li H, Tong J J, Li W Q 2013 Acta Phys. Sin. 62 112901(in Chinese) [冯丙辰, 方晟, 张立国, 李红, 童节娟, 李文茜 2013 物理学报 62 112901]
[12] Yin W, Morgan S, Yang J F, Zhang Y 2010 Visual Communications and Image Processing (International Society for Optics and Photonics) p77440K
[13] Romberg J 2009 SIAM. J. Imaging Sci. 2 1098
[14] Guo J B, Xu X Z, Shi Q H, Hu T H 2013 Acta Phys. Sin. 62 110508(in Chinese) [郭静波, 徐新智, 史启航, 胡铁华 2013 物理学报 62 110508]
[15] Yang J, Zhang Y 2011 SIAM. J. Sci. Comp. 33 250
[16] Chen G, Mao Y, Chui C K 2004 Chaos Solitons & Fractals 21 749
[17] Badea B, Vlad A 2006 Computational Science and Its Applications (Berlin: Springer Berlin Heidelberg) p1166
[18] Hoeffding W 1963 J. American Stat. Assoc. 58 13
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