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Introduction to Compressed Sensing and Sparse Filtering

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Compressed Sensing & Sparse Filtering

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Compressed sensing is a concept bearing far-reaching implications to signal acquisition and recovery which yet continues to penetrate various engineering and scientific domains. Presently, there is a wealth of theoretical results that extend the basic ideas of compressed sensing essentially making analogies to notions from other fields of mathematics. The objective of this chapter is to introduce the reader to the basic theory of compressed sensing as emanated in the first few works on the subject. The first part of this chapter is therefore a concise exposition to compressed sensing which requires no prior background. The second half of this chapter slightly extends the theory and discusses its applicability to filtering of dynamic sparse signals.

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Author information

Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Avishy Y. Carmi

  2. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    Lyudmila S. Mihaylova

  3. Department of Engineering, University of Cambridge, Cambridge, United Kingdom

    Simon J. Godsill

Authors
  1. Avishy Y. Carmi
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  2. Lyudmila S. Mihaylova
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  3. Simon J. Godsill
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Corresponding author

Correspondence to Avishy Y. Carmi .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Nanyang Technical University, Singapore, Singapore

    Avishy Y. Carmi

  2. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    Lyudmila Mihaylova

  3. Department of Engineering, University of Cambridge, Cambridge, United Kingdom

    Simon J. Godsill

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Carmi, A.Y., Mihaylova, L.S., Godsill, S.J. (2014). Introduction to Compressed Sensing and Sparse Filtering. In: Carmi, A., Mihaylova, L., Godsill, S. (eds) Compressed Sensing & Sparse Filtering. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38398-4_1

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  • DOI: https://doi.org/10.1007/978-3-642-38398-4_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38397-7

  • Online ISBN: 978-3-642-38398-4

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