The paper presents the possibility of applying a new class ofmathematical methods, known as Compressive Sensing (CS) for recovering thesignal from a small set of measured samples. CS allows the faithful recon-struction of the original signal back from fewer random measurements bymaking use of some non-linear reconstruction techniques. Since of all thesefeatures, CSfinds its applications especially in the areas where, sensing is timeconsuming or power constrained. An electromagnetic interference measurementis afield where the CS technique can be used. In this case, a sparse signaldecomposition based on matching pursuit (MP) algorithm, which decomposes asignal into a linear expansion of element chirplet functions selected from acomplete and redundant time-frequency dictionary is applied. The presentedpaper describes both the fundamentals of CS and how to implement MP for CSreconstruction in relation to non-stationary signals.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-030-11187-8
- Category
- Publikacja monograficzna
- Type
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language
- angielski
- Publication year
- 2019
