NDLI: Multicomponent AM–FM signal analysis based on sparse approximation

Content Provider	IET Digital Library
Author	Diop, El Hadji S. Skretting, Karl Boudraa, Abdel Ouahab
Abstract	The authors propose in this work to formulate multicomponent amplitude modulation (AM)–frequency modulation (FM) representations as a sparse approximation (SA) problem, which is solved using orthogonal matching pursuits (OMP) and least angle regression (LARS). The approach appears to be an efficient tool to separate the frequency content of linear and non-linear signals and behaves in a better way like the well-known data-driven empirical mode decomposition (EMD). Contrary to related works where AM–FM models and/or EMD are used to improve sparsity or learned dictionaries, they propose instead a sparse approach framework in AM–FM modelling and EMD. Recasting EMD as a SA problem provides a new framework towards its comprehension, formulation, and convergence since OMP and LARS converge. Doing so presents an alternative to getting rid of the interpolation process that EMD terribly suffers from. Conducted experiments show noticeable improvements in terms of frequency separation of linear and non-linear signals, tone separation capability and robustness in a noisy environment.
Starting Page	32
Ending Page	43
Page Count	12
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 1, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0110
Journal	IET Signal Processing
Publisher Date	2019-10-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Amplitude Modulation AM–FM Modelling AM–FM Models AM–FM Signal Analysis Approximation Theory Data-driven Empirical Mode Decomposition Digital Signal Processing EMD Frequency Content Frequency Modulation Frequency Separation Interpolation Interpolation And Function Approximation Knowledge Engineering Technique LARS Learning in AI Least Angle Regression Multicomponent Amplitude Modulation–frequency Modulation Representations Nonlinear Signal Numerical Analysis OMP Regression Analysis SA Problem Signal Processing Signal Processing And Detection Signal Representation Sparse Approach Framework Sparse Approximation Problem Statistics Time-frequency Analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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