Compressed Remote Sensing of Sparse Objects

The linear inverse source and scattering problems are studied from the perspective of compressed sensing. By introducing the sensor as well as target ensembles, the maximum number of recoverable targets is proved to be at least proportional to the number of measurement data modulo a logsquare factor with overwhelming probability. Important contributions include the discoveries of the threshold aperture, consistent with the classical Rayleigh criterion, and the incoherence effect induced by random antenna locations. The predictions of theorems are confirmed by numerical simulations.

[1]  R. Baraniuk,et al.  Compressive Radar Imaging , 2007, 2007 IEEE Radar Conference.

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

[3]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

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

[5]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

[6]  Thomas Strohmer,et al.  General Deviants: An Analysis of Perturbations in Compressed Sensing , 2009, IEEE Journal of Selected Topics in Signal Processing.

[7]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[8]  Deanna Needell,et al.  Greedy signal recovery review , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[9]  John C. Curlander,et al.  Synthetic Aperture Radar: Systems and Signal Processing , 1991 .

[10]  Joel A. Tropp,et al.  Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.

[11]  Junfeng Yang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

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

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

[14]  J. Seidel,et al.  BOUNDS FOR SYSTEMS OF LINES, AND JACOBI POLYNOMIALS , 1975 .

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

[16]  A. Tolstoy,et al.  Matched Field Processing for Underwater Acoustics , 1992 .

[17]  Ali Cafer Gürbüz,et al.  Compressive sensing for subsurface imaging using ground penetrating radar , 2009, Signal Process..

[18]  D. Malacara-Hernández,et al.  PRINCIPLES OF OPTICS , 2011 .

[19]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[20]  J. Tropp Corrigendum in “Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise” [Mar 06 1030-1051] , 2009 .

[21]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

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

[23]  Arthur B. Baggeroer,et al.  An overview of matched field methods in ocean acoustics , 1993 .

[24]  Wai Lam Chan,et al.  A single-pixel terahertz imaging system based on compressed sensing , 2008 .

[25]  Emmanuel J. Cand The Restricted Isometry Property and Its Implications for Compressed Sensing , 2008 .

[26]  A. Fannjiang,et al.  Compressive inverse scattering: II. Multi-shot SISO measurements with born scatterers , 2010 .

[27]  A TroppJoel Corrigendum in "Just relax: Convex programming methods for identifying sparse signals in noise" , 2009 .

[28]  A. Fannjiang,et al.  Compressive inverse scattering: I. High-frequency SIMO/MISO and MIMO measurements , 2009, 0906.5405.

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

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

[31]  Albert Fannjiang,et al.  Multi-frequency imaging of multiple targets in Rician fading channels: stability and resolution , 2007 .

[32]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[33]  Knut Sølna,et al.  Synthetic Aperture Imaging of Multiple Point Targets in Rician Fading Media , 2009, SIAM J. Imaging Sci..

[34]  W. Hoeffding Probability Inequalities for sums of Bounded Random Variables , 1963 .

[35]  C. Tropea,et al.  Light Scattering from Small Particles , 2003 .

[36]  Albert Fannjiang,et al.  Compressive Imaging of Subwavelength Structures , 2009, SIAM J. Imaging Sci..

[37]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[38]  P. Waterman,et al.  MULTIPLE SCATTERING OF WAVES , 1961 .

[39]  H. V. Hulst Light Scattering by Small Particles , 1957 .

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

[41]  J. Tropp On the conditioning of random subdictionaries , 2008 .

[42]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity , 2008, ArXiv.

[43]  Lloyd R. Welch,et al.  Lower bounds on the maximum cross correlation of signals (Corresp.) , 1974, IEEE Trans. Inf. Theory.

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