Multicomponent AM-FM signals analysis based on EMD-B-splines ESA

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• 作者：Abdelkhalek Bouchikhi^a ; ^b ; ^{bouchikhi@enib.fr} ; Abdel-Ouahab Boudraa^a ; ^{boudra@ecole-navale.fr}
• 关键词：Empirical Mode Decomposition ; Teager&ndash ; Kaiser operator ; Energy Separation Algorithm ; Hilbert transform ; AM&ndash ; FM modeling
• 刊名：Signal Processing
• 出版年：2012
• 期刊代码：162_01651684
• 类别：et
• 出版时间：September, 2012
• 卷：92
• 期：9
• 页码：2214-2228
• 文件大小：2436 K

摘要

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 multicomponent 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 effectiveness of the proposed approach in terms of accuracy and robustness against noise to track IF and IA features of a multicomponent AM-FM signal.