The restricted isometry property for time–frequency structured random matrices

This paper establishes the restricted isometry property for a Gabor system generated by n2 time–frequency shifts of a random window function in n dimensions. The sth order restricted isometry constant of the associated n × n2 Gabor synthesis matrix is small provided that s ≤ cn2/3 / log2n. This bound provides a qualitative improvement over previous estimates, which achieve only quadratic scaling of the sparsity s with respect to n. The proof depends on an estimate for the expected supremum of a second-order chaos.

[1]  Justin K. Romberg,et al.  Compressive Sensing by Random Convolution , 2009, SIAM J. Imaging Sci..

[2]  M. Talagrand The Generic Chaining , 2005 .

[3]  Rachel Ward,et al.  New and Improved Johnson-Lindenstrauss Embeddings via the Restricted Isometry Property , 2010, SIAM J. Math. Anal..

[4]  Holger Rauhut Stability Results for Random Sampling of Sparse Trigonometric Polynomials , 2008, IEEE Transactions on Information Theory.

[5]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[6]  Aicke Hinrichs,et al.  Johnson‐Lindenstrauss lemma for circulant matrices* * , 2010, Random Struct. Algorithms.

[7]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[8]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[9]  F. Krahmer,et al.  Uncertainty in time–frequency representations on finite Abelian groups and applications , 2006, math/0611493.

[10]  Simon Foucart,et al.  Hard Thresholding Pursuit: An Algorithm for Compressive Sensing , 2011, SIAM J. Numer. Anal..

[11]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[12]  Juan Luis Varona,et al.  Complex networks and decentralized search algorithms , 2006 .

[13]  Thomas Strohmer,et al.  High-Resolution Radar via Compressed Sensing , 2008, IEEE Transactions on Signal Processing.

[14]  R. DeVore,et al.  Compressed sensing and best k-term approximation , 2008 .

[15]  A. Ron Review of An introduction to Frames and Riesz bases, applied and numerical Harmonic analysis by Ole Christensen Birkhäuser, Basel, 2003 , 2005 .

[16]  Thomas Strohmer,et al.  GRASSMANNIAN FRAMES WITH APPLICATIONS TO CODING AND COMMUNICATION , 2003, math/0301135.

[17]  Holger Rauhut,et al.  Circulant and Toeplitz matrices in compressed sensing , 2009, ArXiv.

[18]  Holger Rauhut,et al.  The Gelfand widths of ℓp-balls for 0 , 2010, ArXiv.

[19]  Richard G. Baraniuk,et al.  Random Filters for Compressive Sampling and Reconstruction , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[20]  Holger Rauhut,et al.  Compressive Sensing with structured random matrices , 2012 .

[21]  Justin Romberg,et al.  Compressive Sampling via Random Convolution , 2007 .

[22]  William O. Alltop,et al.  Complex sequences with low periodic correlations , 1980 .

[23]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[24]  Milica Stojanovic,et al.  Underwater acoustic communications , 1995, Proceedings of Electro/International 1995.

[25]  H. Rauhut,et al.  Sparse Legendre expansions via $\ell_1$ minimization , 2010, 1003.0251.

[26]  Andrej Yu. Garnaev,et al.  On widths of the Euclidean Ball , 1984 .

[27]  H. Rauhut Compressive Sensing and Structured Random Matrices , 2009 .

[28]  E E Fenimore,et al.  New family of binary arrays for coded aperture imaging. , 1989, Applied optics.

[29]  Laurent Demanet,et al.  Matrix Probing and its Conditioning , 2012, SIAM J. Numer. Anal..

[30]  J. Azaïs,et al.  Level Sets and Extrema of Random Processes and Fields , 2009 .

[31]  Massimo Fornasier,et al.  Compressive Sensing and Structured Random Matrices , 2010 .

[32]  A. Buchholz Operator Khintchine inequality in non-commutative probability , 2001 .

[33]  M. Talagrand New concentration inequalities in product spaces , 1996 .

[34]  Stephen J. Wright,et al.  Toeplitz-Structured Compressed Sensing Matrices , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[35]  Holger Rauhut,et al.  Sparsity in Time-Frequency Representations , 2007, ArXiv.

[36]  D. Donoho,et al.  Counting faces of randomly-projected polytopes when the projection radically lowers dimension , 2006, math/0607364.

[37]  Holger Rauhut,et al.  Sparse Legendre expansions via l1-minimization , 2012, J. Approx. Theory.

[38]  Niklas Grip,et al.  A discrete model for the efficient analysis of time-varying narrowband communication channels , 2008, Multidimens. Syst. Signal Process..

[39]  William O. Alltop,et al.  Complex sequences with low periodic correlations (Corresp.) , 1980, IEEE Trans. Inf. Theory.

[40]  J. Romberg,et al.  Restricted Isometries for Partial Random Circulant Matrices , 2010, arXiv.org.

[41]  S. Foucart Sparse Recovery Algorithms: Sufficient Conditions in Terms of RestrictedIsometry Constants , 2012 .

[42]  J. Lawrence,et al.  Linear Independence of Gabor Systems in Finite Dimensional Vector Spaces , 2005 .

[43]  Luis M. Correia,et al.  Wireless Flexible Personalized Communications , 2001 .

[44]  P. Bello Characterization of Randomly Time-Variant Linear Channels , 1963 .

[45]  Massimo Fornasier,et al.  Theoretical Foundations and Numerical Methods for Sparse Recovery , 2010, Radon Series on Computational and Applied Mathematics.

[46]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[47]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..

[48]  D. Middleton,et al.  Channel Modeling and Threshold Signal Processing in Underwater Acoustics: An Analytical Overview , 1987 .

[49]  G. Pisier Remarques sur un résultat non publié de B. Maurey , 1981 .

[50]  Rebecca Willett,et al.  Fast disambiguation of superimposed images for increased field of view , 2008, 2008 15th IEEE International Conference on Image Processing.

[51]  P. MassartLedoux Concentration Inequalities Using the Entropy Method , 2002 .

[52]  S. Foucart A note on guaranteed sparse recovery via ℓ1-minimization , 2010 .

[53]  O. Christensen An introduction to frames and Riesz bases , 2002 .

[54]  Holger Rauhut,et al.  The Gelfand widths of lp-balls for 0p<=1 , 2010, J. Complex..

[55]  Holger Rauhut,et al.  Edinburgh Research Explorer Identification of Matrices Having a Sparse Representation , 2022 .

[56]  Matthias Patzold,et al.  Mobile Fading Channels: Modelling,Analysis and Simulation , 2001 .

[57]  E. Candès The restricted isometry property and its implications for compressed sensing , 2008 .

[58]  Deanna Needell,et al.  Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit , 2007, Found. Comput. Math..

[59]  B. Carl Inequalities of Bernstein-Jackson-type and the degree of compactness of operators in Banach spaces , 1985 .

[60]  M. Rudelson,et al.  On sparse reconstruction from Fourier and Gaussian measurements , 2008 .

[61]  E.J. Candes Compressive Sampling , 2022 .

[62]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .

[63]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[64]  J. Kuelbs Probability on Banach spaces , 1978 .

[65]  Mike E. Davies,et al.  Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.

[66]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[67]  Robert D. Nowak,et al.  Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation , 2010, IEEE Transactions on Information Theory.

[68]  Pierre Vandergheynst,et al.  Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.

[69]  Mark A. Richards,et al.  Fundamentals of Radar Signal Processing , 2005 .

[70]  Bu‐Chin Wang,et al.  Fundamentals of Radar , 2008 .

[71]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[72]  Jan Vyb'iral A variant of the Johnson-Lindenstrauss lemma for circulant matrices , 2010, 1002.2847.

[73]  S. Mendelson,et al.  Uniform Uncertainty Principle for Bernoulli and Subgaussian Ensembles , 2006, math/0608665.