Comparison of methods for signal analysis in the time-frequency domain

Robin Alvarez, Erick Borbor, Felipe Grijalva

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Resumen

This paper shows the most relevant results of the comparison of four signal analysis methods in the time-frequency domain: Short Time Fourier Transform, Wigner-Ville Distribution, Wavelets and Matching Pursuit, using an artificially created signal. This was done in order to look for the advantages and disadvantages of each of these methods in terms of frequency resolution, time resolution, detection and computational load. For the comparison, five experiments were performed with the artificial signal. Each new test demands more strict conditions for time resolution, frequency resolution and component detection due to the amplitude reduces and frequency separation decreases among components. The results show that, the best method in terms of frequency resolution, detection and computational load is the Short Time Fourier Transform. On the other hand, Bump Wavelet, which is also the best among the wavelets analyzed, has the best time resolution allowing to distinguish the start and end times of each component of the signal with excellent precision for each of the tests performed.

Idioma originalInglés
Título de la publicación alojada2019 IEEE 4th Ecuador Technical Chapters Meeting, ETCM 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728137643
DOI
EstadoPublicada - nov. 2019
Publicado de forma externa
Evento4th IEEE Ecuador Technical Chapters Meeting, ETCM 2019 - Guayaquil, Ecuador
Duración: 13 nov. 201915 nov. 2019

Serie de la publicación

Nombre2019 IEEE 4th Ecuador Technical Chapters Meeting, ETCM 2019

Conferencia

Conferencia4th IEEE Ecuador Technical Chapters Meeting, ETCM 2019
País/TerritorioEcuador
CiudadGuayaquil
Período13/11/1915/11/19

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