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Signal Reconstruction from Sparse Measurements Using Compressive Sensing Technique

  • Beata Palczynska
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 548)

Abstract

The paper presents the possibility of applying a new class of mathematical methods, known as Compressive Sensing (CS) for recovering the signal from a small set of measured samples. CS allows the faithful reconstruction of the original signal back from fewer random measurements by making use of some non-linear reconstruction techniques. Since of all these features, CS finds its applications especially in the areas where, sensing is time consuming or power constrained. An electromagnetic interference measurement is a field where the CS technique can be used. In this case, a sparse signal decomposition based on matching pursuit (MP) algorithm, which decomposes a signal into a linear expansion of element chirplet functions selected from a complete and redundant time-frequency dictionary is applied. The presented paper describes both the fundamentals of CS and how to implement MP for CS reconstruction in relation to non-stationary signals.

Keywords

Compressive sensing Matching pursuit 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Gdansk University of TechnologyGdanskPoland
  2. 2.Gdynia Maritime UniversityGdyniaPoland

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