A New Source Extraction Algorithm for Cyclostationary Sources

Capdessus, Cécile; Nandi, Asoke K.; Bouguerriou, N.

doi:10.1007/978-3-540-74494-8_19

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Boustany and Antoni [13] propose a method for blind extraction of one cyclostationay signal using a subspace decomposition of the observation signals via their cyclic statistics. Capdessus et al [14] propose an algorithm for the extraction a signal of interest which is cyclostationary one and its cyclic frequency is a priori known. This algorithm relies on second-order statistics of the observation signals.…”

Section: Introductionmentioning

confidence: 99%

Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

Brahmi

Ghennioui

Corbier

et al. 2017

Mathematical Problems in Engineering

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We propose a new method for blind source separation of cyclostationary sources, whose cyclic frequencies are unknown and may share one or more common cyclic frequencies. The suggested method exploits the cyclic correlation function of observation signals to compose a set of matrices which has a particular algebraic structure. The aforesaid matrices are automatically selected by proposing two new criteria. Then, they are jointly diagonalized so as to estimate the mixing matrix and retrieve the source signals as a consequence. The nonunitary joint diagonalization (NU-JD) is ensured by Broyden-Fletcher-Goldfarb-Shanno (BFGS) method which is the most commonly used update strategy for implementing a quasi-Newton technique. The efficiency of the method is illustrated by numerical simulations in digital communications context, which show good performances comparing to other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

Brahmi

Ghennioui

Corbier

et al. 2017

Mathematical Problems in Engineering

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“…Ghaderi et al [22] implement an iterative diagonalisation method of the cyclic covariance matrix estimated at the cyclic frequency of the SOI and Xiang [23] minimises a cyclic statistics based cost function with a constraint on the power of the estimate. Preliminary versions of the method presented here have appeared previously in [24,25] and an application to ECG signals was presented in [25,26].…”

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confidence: 99%

Extraction of a cyclostationary source using a new cost function without pre-whitening

Capdessus

Nandi

2011

Signal Processing

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The proposed method aims to extract a cyclostationary source, whose cyclic frequency is a priori known, from a set of additive mixtures. The other sources may be either stationary or cyclostationary as long as their cyclic frequencies are different from that of the source to be extracted. The method does not require pre-whitening and consists in minimising a criterion based on stationary and cyclostationary second order statistics of the observations; this method is labeled as Second Order Cyclostationary Statistics Optimization Criterion (SOC 2 ). The relevance of this criterion is proven theoretically in the general case of N sources by P sensors, with N P ≥ . Other properties of the algorithm such as its accuracy and its robustness against additive noise or strong interferences are studied through a set of simulations.

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A new method for blind extraction of cyclostationary signals

Wang

Chen

Zhou

et al. 2011

Journal of Vibration and Control

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This paper illustrates the significance of the purity of cyclostationary signals and presents a new method for blind extraction of these signals. The advantage of the method can not only extract the cyclostationary signals from low order to high order in turn but also perform algorithm without knowing cyclic frequencies of the extracted signals. The effectiveness of the proposed method is finally demonstrated by computer simulations and experiments.

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A New Source Extraction Algorithm for Cyclostationary Sources

Cited by 3 publications

References 6 publications

Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

Extraction of a cyclostationary source using a new cost function without pre-whitening

A new method for blind extraction of cyclostationary signals

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