DocumentCode :
3523953
Title :
Comparing Gaussian and chirplet dictionaries for time-frequency analysis using matching pursuit decomposition
Author :
Ghofrani, S. ; McLernon, D.C. ; Ayatollahi, Ahmad
Author_Institution :
Sch. of Electron. & Electr. Eng., Leeds Univ., UK
fYear :
2003
fDate :
14-17 Dec. 2003
Firstpage :
713
Lastpage :
716
Abstract :
As convergence of the matching pursuit (MP) decomposition is not dependent upon the type of atom used, we are free to assume different dictionaries. In this paper we compare the chirplet and Gaussian atoms, because both always give positive values for the Wigner-Ville distribution, and therefore the MP distribution is also always positive (as mathematically required). We show that when the MP decomposition is applied to analyze a time-varying signal, the chirplet atom is better than the Gaussian atom in tracking the instantaneous frequency. Although computational more demanding, we see that it also has a faster convergence rate. Finally, the resolution of the extracted MP distribution with the chirplet atom can also be clearly observed to be superior.
Keywords :
Gaussian processes; Wigner distribution; iterative methods; signal processing; time-frequency analysis; Gaussian atom; Gaussian dictionary; Wigner-Ville distribution; chirplet atom; chirplet dictionary; matching pursuit decomposition; time-frequency analysis; time-varying signal; Chirp; Convergence; Dictionaries; Frequency domain analysis; Iterative algorithms; Matching pursuit algorithms; Signal analysis; Signal resolution; Time frequency analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Information Technology, 2003. ISSPIT 2003. Proceedings of the 3rd IEEE International Symposium on
Print_ISBN :
0-7803-8292-7
Type :
conf
DOI :
10.1109/ISSPIT.2003.1341220
Filename :
1341220
Link To Document :
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