Compressed Sensing, Sparse Inversion, and Model Mismatch

  • Ali PezeshkiEmail author
  • Yuejie Chi
  • Louis L. Scharf
  • Edwin K. P. Chong
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


The advent of compressed sensing theory has revolutionized our view of imaging, as it demonstrates that subsampling has manageable consequences for image inversion, provided that the image is sparse in an apriori known dictionary. For imaging problems in spectrum analysis (estimating complex exponential modes), and passive and active radar/sonar (estimating Doppler and angle of arrival), this dictionary is usually taken to be a DFT basis (or frame) constructed for resolution of 2πn, with n a window length, array length, or pulse-to-pulse processing length. However, in reality no physical field is sparse in a DFT frame or in any apriori known frame. No matter how finely we grid the parameter space (e.g., frequency, delay, Doppler, and/or wavenumber) the sources may not lie in the center of the grid cells and consequently there is always mismatch between the assumed and the actual frames for sparsity. But what is the sensitivity of compressed sensing to mismatch between the physical model that generated the data and the mathematical model that is assumed in the sparse inversion algorithm? In this chapter, we study this question. The focus is on the canonical problem of DFT inversion for modal analysis.


Compress Sensing Linear Prediction Orthogonal Match Pursuit Basis Pursuit Basis Mismatch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ali Pezeshki
    • 1
    Email author
  • Yuejie Chi
    • 2
  • Louis L. Scharf
    • 3
  • Edwin K. P. Chong
    • 1
  1. 1.Department of Electrical and Computer Engineering, and Department of MathematicsColorado State UniversityFort CollinsUSA
  2. 2.Department of Electrical and Computer Engineering, and Department of Biomedical InformaticsThe Ohio State UniversityColumbusUSA
  3. 3.Department of MathematicsColorado State UniversityFort CollinsUSA

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