Algorithms for blind components separation and extraction from the time-frequency distribution of their mixture

Authors:
B. Barkat;K. Abed-Meraim
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;Signal and Image Processing Department, École National Supérieure des Télécommunications, Telecom Paris, Paris, France
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

Citing 7
Cited 0

Time-frequency analysis: theory and applications

Time-frequency analysis: theory and applications
Analysis of polynomial-phase signals by the integrated generalizedambiguity function

IEEE Transactions on Signal Processing
Analysis and synthesis of multicomponent signals using positivetime-frequency distributions

IEEE Transactions on Signal Processing
Instantaneous frequency estimation of polynomial FM signals usingthe peak of the PWVD: statistical performance in the presence ofadditive gaussian noise

IEEE Transactions on Signal Processing
High spectral resolution time-frequency distribution kernels

IEEE Transactions on Signal Processing
A high-resolution quadratic time-frequency distribution formulticomponent signals analysis

IEEE Transactions on Signal Processing
Resolution measure criteria for the objective assessment of the performance of quadratic time-frequency distributions

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.02

Visualization

Abstract

We propose novel algorithms to select and extract separately all the components, using the time-frequency distribution (TFD), of a given multicomponent frequency-modulated (FM) signal. These algorithms do not use any a priori information about the various components. However, their performances highly depend on the cross-terms suppression ability and high time-frequency resolution of the considered TFD. To illustrate the usefulness of the proposed algorithms, we applied them for the estimation of the instantaneous frequency coefficients of a multicomponent signal and the results are compared with those of the higher-order ambiguity function (HAF) algorithm. Monte Carlo simulation results show the superiority of the proposed algorithms over the HAF.