A Mixing Matrix Estimation Algorithm for the Time-Delayed Mixing Model of the Underdetermined Blind Source Separation Problem

Ye, Fang; Chen, Jie; Gao, Lipeng; Nie, Wei; Sun, Qian

doi:10.1007/s00034-018-0930-5

Cited by 9 publications

(13 citation statements)

References 25 publications

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“…It can be seen from ( 6) that the ratio of X 2 (t, f ) n /X 1 (t, f ) n actually calculated is a complex number rather than a real number, and the traditional clustering algorithm can not be used directly to deal with the problem of complex number clustering. The traditional clustering algorithm is no longer suitable in the complex field, hence the transformation matrix T is introduced to solve the clustering problem of complex numbers [16].…”

Section: Mixing Matrix Estimation For Ubss a Ssp Detectionmentioning

confidence: 99%

“…In order to prove the superiority of the proposed algorithm, this paper compares the proposed algorithm with the algorithm in [16] and [7]. Fig.…”

Section: Simulation Of Different Mixing Matrix Estimation Algorithmsmentioning

confidence: 99%

“…An SSP detection algorithm is proposed in reference [15], which can effectively estimate the mixing matrix by using SSPs, but large computational complexity is the main defects. Ye [16] proposed an SSP detection algorithm based on the transformation matrix, and then determined the number of source signals by looking for the peak value of the potential function, but the selection of peak value is greatly affected by the noise. The algorithms in [17], [18], [19], [20], [21] all use SSP detection to improve the sparsity of the source signals.…”

Section: Introductionmentioning

confidence: 99%

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Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Bai

Shi³

et al. 2021

IEEE Access

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An original algorithm for underdetermined mixing matrix estimation is proposed in this paper, which can estimate the mixing matrix effectively when the number of source signals is unknown. Firstly, a new single source point (SSP) detection algorithm based on transformation matrix is proposed, which can effectively detect single source time-frequency (TF) points by using the characteristics of complex ratio and improve the sparsity of source signals. In view of the fact that the number of source signals is unknown, a novel estimation algorithm based on element sorting is proposed, which can significantly improve the estimation accuracy of the number of source signals. Finally, the mixing matrix is estimated by using the cluster center obtained by agglomerative hierarchical clustering (AHC) algorithm. The simulation results show that the proposed algorithm can improve the estimation accuracy of the number of source signals and underdetermined mixing matrix obviously, and the algorithm has higher robustness compared with other algorithms.

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Section: Mixing Matrix Estimation For Ubss a Ssp Detectionmentioning

confidence: 99%

“…In order to prove the superiority of the proposed algorithm, this paper compares the proposed algorithm with the algorithm in [16] and [7]. Fig.…”

Section: Simulation Of Different Mixing Matrix Estimation Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Bai

Shi³

et al. 2021

IEEE Access

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“…A modification of sparse component analysis based on the time-frequency domain was given in [29]. The blind source separation problem in the time-frequency domain has also been investigated in [30], as well as in [31]: the mixed signals were transformed from the time domain to the time-frequency domain. Both the effectiveness and superiority of the proposed algorithm were verified, but under the assumption that there are several sensors and that there are single-source points.…”

Section: Introductionmentioning

confidence: 99%

“…Both methods are dependent on the number of sensors. Other methods dependent on the number of sensors for blind source separation based on the mixing matrix are presented in [32,33].…”

Section: Introductionmentioning

confidence: 99%

A novel approach to extracting useful information from noisy TFDs using 2D local entropy measures

Vrankovic

Lerga

Saulig

2020

EURASIP J. Adv. Signal Process.

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The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi entropy calculated inside adaptive, data-driven 2D regions, the sizes of which are calculated utilizing the improved, relative intersection of confidence intervals (RICI) algorithm. One of the advantages of the proposed technique is that it does not require any prior knowledge on the signal, its components, or noise, but rather the processing is performed on the noisy signal mixtures. Also, it is shown that the method is robust to the selection of time-frequency distributions (TFDs). It has been tested for different signal-to-noise-ratios (SNRs), both for synthetic and real-life data. When compared to fixed TFD thresholding, adaptive TFD thresholding based on RICI rule and the 1D entropy-based approach, the proposed adaptive method significantly increases classification accuracy (by up to 11.53%) and F1 score (by up to 7.91%). Hence, this adaptive, data-driven, entropy-based technique is an efficient tool for extracting useful information from noisy data in the time-frequency domain.

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Mixing Matrix Estimation Algorithm for Time-Varying Radar Signals in a Dynamic System Under UBSS Model

Bai

Zhou

et al. 2021

Circuits Syst Signal Process

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A Mixing Matrix Estimation Algorithm for the Time-Delayed Mixing Model of the Underdetermined Blind Source Separation Problem

Cited by 9 publications

References 25 publications

Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

A novel approach to extracting useful information from noisy TFDs using 2D local entropy measures

Mixing Matrix Estimation Algorithm for Time-Varying Radar Signals in a Dynamic System Under UBSS Model

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