Signal separation using second- and high-order statistics

Fahmy, Mahmoud; El-Raheem, G.M.A.; El-Sallam, Amar A.

doi:10.1002/(sici)1097-007x(200005/06)28:3<225::aid-cta102>3.0.co;2-1

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…The most widely used strategy to decouple the signals y 1 , y 2 , is to decorrelate them [12], [13] based on the assumption that the original source signals are statistically independent. This is achieved by setting to zero (or at least minimizing), the second commulant and possibly higher order commulants.…”

Section: Problem Formulationmentioning

confidence: 99%

“…It is easy to show that (13) In time, by successive application of the update in (9) the performance index in (7) is minimized, and the mixed signals are decoupled.…”

Section: Adaptive Lms Signal Decouplingmentioning

confidence: 99%

“…To prevent the zero solution, the update equation is modified so that, besides the decorrelation, the network F should also maintain both the decoupled signals at a specified power level [13]. The performance index in (7) …”

Section: Adaptive Lms Signal Decouplingmentioning

confidence: 99%

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Adaptive multichannel LMS signal decoupling

Haweel

2015

2015 International Conference on Communications, Signal Processing, and Their Applications (ICCSPA'15)

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Multichannel signal separation is a problem frequently encountered in many signal processing and communications applications. A simple and efficient sequential LMS adaptive multichannel signal decoupling algorithm is presented. It is based on minimizing the second commulant for the received mixed signals, although it can also be applied to minimize higher order commulants. The algorithm employs instantaneous signal cross products as estimates for the true cross-correlation based on ensemble averaging. No time averaging is required as in the case of most existing methods. The power of the decoupled output signals may be adjusted to the required levels. The proposed algorithm is fast, computationally efficient and suitable for real time applications. Analysis and simulations are provided to justify the efficiency of the proposed algorithm.

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Section: Problem Formulationmentioning

confidence: 99%

“…It is easy to show that (13) In time, by successive application of the update in (9) the performance index in (7) is minimized, and the mixed signals are decoupled.…”

Section: Adaptive Lms Signal Decouplingmentioning

confidence: 99%

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Adaptive multichannel LMS signal decoupling

Haweel

2015

2015 International Conference on Communications, Signal Processing, and Their Applications (ICCSPA'15)

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“…The sources are assumed to be mutually independent and no information exits about their distribution. This subject has been extensively studied for the linear memoryless mixture, where separation algorithms are mainly based on Independent Component Analysis (ICA), [1][2][3][4][5][6][7]11]. The basic concept of ICA techniques stems from the fact that separation is achieved if the output's negentropy is maximized.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation Technique for Linear Mixtures Based on Accurate Estimation of Probability Density Function

Fahmy

Mohammed

Bahran

2010

JES. Journal of Engineering Sciences

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“…The function of this system is to make the outputs yip) completely independent. In [3,4], this condition has been achieved through decorrelating the output time signals. However un-correlation doesn't mean independence.…”

Section: Introductionmentioning

confidence: 99%

Blind source separation using time-frequency concepts

Fahmy

Amin

Hasan

et al.

Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, 2004.

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ribsiract -In this paper two methuds are described for Blind Signal Separation (BSq based on Time Frequency Distributions F D s of the received mixed signals. Cunsequently, they are must suitable io deal with non-siationav mixed signals. The f i s t method determines the &coupling system through sening to zero coeficienrr of the ambiguivyfrmrctwn ojthe received mixed signals at the edges of its distribution plane. Computations have revealed that this appronch is on& successfl when their TFD signatures, are pmtialh oyerlapped. when the lFD signahires are highly overlqped an oltemate approach is described that determines the elements of the decoupling system through decorrelating rhe respective rows of rhe received TFD &U matrices. Illustrarive exampler are giwn showing that the proposed approaches work when other approaches based on higher order statistics HOSfail tu yield satisfactofy results.

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Signal separation using second- and high-order statistics

Cited by 6 publications

References 3 publications

Adaptive multichannel LMS signal decoupling

Adaptive multichannel LMS signal decoupling

Blind Source Separation Technique for Linear Mixtures Based on Accurate Estimation of Probability Density Function

Blind source separation using time-frequency concepts

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