Multicomponent AM-FM signals analysis based on EMD-B-splines ESA
| hal.structure.identifier | Institut de Recherche de l'Ecole Navale [IRENAV] | |
| dc.contributor.author | BOUCHIKHI, Abdelkhalek | |
| hal.structure.identifier | Institut de Recherche de l'Ecole Navale [IRENAV] | |
| dc.contributor.author | BOUDRAA, Abdel-Ouahab | |
| dc.date.accessioned | 2021-05-14T09:58:16Z | |
| dc.date.available | 2021-05-14T09:58:16Z | |
| dc.date.issued | 2012-09 | |
| dc.identifier.issn | 0165-1684 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/77944 | |
| dc.description | In this paper a signal analysis framework for estimating time-varying amplitude and frequency functions of multicomponent amplitude and frequency modulated (AM–FM) signals is introduced. This framework is based on local and non-linear approaches, namely Energy Separation Algorithm (ESA) and Empirical Mode Decomposition (EMD). Conjunction of Discrete ESA (DESA) and EMD is called EMD–DESA. A new modified version of EMD where smoothing instead of an interpolation to construct the upper and lower envelopes of the signal is introduced. Since extracted IMFs are represented in terms of B-spline (BS) expansions, a closed formula of ESA robust against noise is used. Instantaneous Frequency (IF) and Instantaneous Amplitude (IA) estimates of a multi- component AM–FM signal, corrupted with additive white Gaussian noise of varying SNRs, are analyzed and results compared to ESA, DESA and Hilbert transform-based algorithms. SNR and MSE are used as figures of merit. Regularized BS version of EMD– ESA performs reasonably better in separating IA and IF components compared to the other methods from low to high SNR. Overall, obtained results illustrate the effective- ness of the proposed approach in terms of accuracy and robustness against noise to track IF and IA features of a multicomponent AM–FM signal. | |
| dc.description.abstractEn | In this paper a signal analysis framework for estimating time-varying amplitude and frequency functions of multicomponent amplitude and frequency modulated (AM–FM) signals is introduced. This framework is based on local and non-linear approaches, namely Energy Separation Algorithm (ESA) and Empirical Mode Decomposition (EMD). Conjunction of Discrete ESA (DESA) and EMD is called EMD–DESA. A new modified version of EMD where smoothing instead of an interpolation to construct the upper and lower envelopes of the signal is introduced. Since extracted IMFs are represented in terms of B-spline (BS) expansions, a closed formula of ESA robust against noise is used. Instantaneous Frequency (IF) and Instantaneous Amplitude (IA) estimates of a multi- component AM–FM signal, corrupted with additive white Gaussian noise of varying SNRs, are analyzed and results compared to ESA, DESA and Hilbert transform-based algorithms. SNR and MSE are used as figures of merit. Regularized BS version of EMD– ESA performs reasonably better in separating IA and IF components compared to the other methods from low to high SNR. Overall, obtained results illustrate the effective- ness of the proposed approach in terms of accuracy and robustness against noise to track IF and IA features of a multicomponent AM–FM signal. | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.subject | AM–FM modeling | |
| dc.subject | Empirical Mode Decomposition | |
| dc.subject | Energy Separation Algorithm | |
| dc.subject | Hilbert transform | |
| dc.subject | Teager–Kaiser operator | |
| dc.subject | Décomposition modale empirique | |
| dc.subject | opérateur de Teager-Kaiser | |
| dc.subject | Algorithme de séparation d'énergie | |
| dc.subject | transformée d'Hilbert | |
| dc.subject | Modèle AM-FM | |
| dc.title.en | Multicomponent AM-FM signals analysis based on EMD-B-splines ESA | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1016/j.sigpro.2012.02.014 | |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
| bordeaux.journal | Signal Processing | |
| bordeaux.page | 2214-2228 | |
| bordeaux.volume | 92 | |
| bordeaux.hal.laboratories | Institut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295 | * |
| bordeaux.issue | 9 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.institution | INRAE | |
| bordeaux.institution | Arts et Métiers | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01090557 | |
| hal.version | 1 | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01090557v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Signal%20Processing&rft.date=2012-09&rft.volume=92&rft.issue=9&rft.spage=2214-2228&rft.epage=2214-2228&rft.eissn=0165-1684&rft.issn=0165-1684&rft.au=BOUCHIKHI,%20Abdelkhalek&BOUDRAA,%20Abdel-Ouahab&rft.genre=article |
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