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Signal Reconstruction from Sparse Measurements Using Compressive Sensing Technique

Abstrakt

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.

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Informacje szczegółowe

Kategoria:
Archiwalna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Tytuł wydania:
Methods and Techniques of Signal Processing in Physical Measurements strony 239 - 247
ISSN:
1876-1100
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Pałczyńska B.: Signal Reconstruction from Sparse Measurements Using Compressive Sensing Technique// Methods and Techniques of Signal Processing in Physical Measurements/ ed. Hanus R., Mazur D., Kreischer C. : Springer, Cham, 2019, s.239-247
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/978-3-030-11187-8_20

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