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Multiple-view time-frequency distribution based on the empirical mode decomposition
- Publication Year :
- 2010
- Publisher :
- Institution of Engineering and Technology (IET), 2010.
-
Abstract
- This paper proposes a composite TFD based on a multiple-view Approach where the IMFs of an EMD are used to construct a series of views in the ambiguity domain by highlighting concentrations of energy in the ambiguity domain and overcoming interferences in TFDs. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated). This study proposes a novel, composite time-frequency distribution (TFD) constructed using a multiple-view approach. This composite TFD utilises the intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) to generate each view that are then combined using the arithmetic mean. This process has the potential to eliminate the inter-component interference generated by a quadratic TFD (QTFD), as the IMFs of the EMD are, in general, monocomponent signals. The formulation of the multiple-view TFD in the ambiguity domain results in faster computation, compared to a convolutive implementation in the time-frequency domain, and a more robust TFD in the presence of noise. The composite TFD, referred to as the EMD-TFD, was shown to generate a heuristically more accurate representation of the distribution of time-frequency energy in a signal. It was also shown to have performance comparable to the Wigner-Ville distribution when estimating the instantaneous frequency of multiple signal components in the presence of noise.
- Subjects :
- Signal processing
Composite time-frequency
Noise (signal processing)
Speech recognition
Time- frequency domain
Multiple signal components
Sonar signal processing
Instantaneous phase
TFD
Hilbert–Huang transform
Time–frequency analysis
Multiple-view time-frequency distribution
Hilbert spectrum
Signal Processing
EMD
Radar signal processing
Wigner-Ville distribution
Electrical and Electronic Engineering
Empirical mode decomposition
Algorithm
Energy (signal processing)
Mathematics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....007e1f0b0205da7b99608109641f39f1