Extreme direction analysis for blind separation of nonnegative signals

Naanaa, Wady

doi:10.1016/j.sigpro.2016.07.016

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…In online BSS methods, parameters should be updated recursively to reduce the computational complexity at each iteration. From equation (13), to obtain ζ, B 1 and B 2 should be adaptively updated.…”

Section: Online Updating Of Separation Indicatormentioning

confidence: 99%

“…In the past decades, blind source separation (BSS) has been a prominent issue in the field of signal processing and neural networks [9], and has been widely used in many practical applications [10][11][12][13][14]. In addition, BSS aims to recover statistically independent source signals from mixed signals without any prior knowledge of the source signals or the transmission channel characteristics [15].…”

Section: Introductionmentioning

confidence: 99%

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Online blind source separation method with adaptive step size in both time-invariant and time-varying cases

Cheng

2020

Meas. Sci. Technol.

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To effectively balance the convergence speed and steady-state error of online blind source separation, this paper develops an online blind source separation method with adaptive step size based on an equivariant adaptive separation via independence (EASI) algorithm. First, we construct a separation indicator from the convergence condition of EASI that can reveal the separation degree of mixed signals. Next, a new forgetting factor suitable for non-stationary cases is designed to reduce the error accumulation of previous data, and the separation indicator can be adaptively updated. To automatically adjust the step size according to separation degree, a nonlinear mapping between the separation indicator and step size is constructed. Finally, numerical and experimental case studies are provided to evaluate the performance of the proposed method, which comparison of results proves to be more effective. The results of numerical case studies show that the step size of the proposed method can be adaptively adjusted in the separation process and the proposed method can effectively balance convergence speed and steady-state error in both time-invariant and time-varying cases. The results of experimental case studies show that the proposed method has higher estimation accuracy.

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Section: Online Updating Of Separation Indicatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online blind source separation method with adaptive step size in both time-invariant and time-varying cases

Cheng

2020

Meas. Sci. Technol.

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“…Among post-processing approaches, blind source separation (BSS) has demonstrated its usefulness in separating sources from mixed signals, due to its simplicity and effectiveness. More importantly, BSS can be utilized without the structure models and the transmission paths that are difficult to be obtained, and therefore BSS has been widely used in practice [4,5,6,7,8,9]. However, most of these methods are mainly designed for (over)determined BSS where the number of sensors is no smaller than that of sources, and thus they may fail when dealing with underdetermined cases.…”

Section: Introductionmentioning

confidence: 99%

A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

Cheng

2019

Sensors

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To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

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“…Typically, the general case may not have perfect identification (apart from the scalability and uniqueness issues). For the general case, minimum volume-based approaches [ 26 , 31 , 32 ], and extreme direction-based approaches [ 33 ] have been proposed to approximately recover the source matrix. Some special cases of sparse structure, apart from the above cases, that can recover original source data have also been studied in the literature, for example see [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Big Data Blind Separation

Syed

2018

Entropy

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Data or signal separation is one of the critical areas of data analysis. In this work, the problem of non-negative data separation is considered. The problem can be briefly described as follows: given X ∈ R m×N , find A ∈ R m×n and S ∈ R n×N + such that X = AS. Specifically, the problem with sparse locally dominant sources is addressed in this work. Although the problem is well studied in the literature, a test to validate the locally dominant assumption is not yet available. In addition to that, the typical approaches available in the literature sequentially extract the elements of the mixing matrix. In this work, a mathematical modeling-based approach is presented that can simultaneously validate the assumption, and separate the given mixture data. In addition to that, a correntropy-based measure is proposed to reduce the model size. The approach presented in this paper is suitable for big data separation. Numerical experiments are conducted to illustrate the performance and validity of the proposed approach.

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Extreme direction analysis for blind separation of nonnegative signals

Cited by 5 publications

References 26 publications

Online blind source separation method with adaptive step size in both time-invariant and time-varying cases

Online blind source separation method with adaptive step size in both time-invariant and time-varying cases

A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

Big Data Blind Separation

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