Multiuser processing using blind source separation methods

Cavalcante, Charles C.; Filho, Danilo Zanatta; Romano, João Marcos Travassos

doi:10.1002/ett.1327

Cited by 4 publications

(5 citation statements)

References 15 publications

(23 reference statements)

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“…This property makes the MDL approach widely used to estimate the number of signals. Unlike the approaches presented by [7] and [8], the MDL does not require any subjective threshold settings in the estimation process. The proposed method is summarized step by step as follows:…”

Section: B Proposed Subspace Projection Methodsmentioning

confidence: 88%

“…We note from (6), (8), and (9) that , where is a scalar. For any control vector , as long as , it is easy to see from (9) that is equivalent to of (4) when .…”

Section: B Signal Cyclostationarity and The Score Algorithmsmentioning

confidence: 99%

“…Next, we briefly describe the LS-SCORE and Cross-SCORE algorithms of [3]. Based on the LS-SCORE algorithm, the optimal weight vector is obtained adaptively as (7) where the reference signal is given by (8) where denotes the control signal and a fixed control vector. The solution of (7) is given by [3] (9)…”

Section: B Signal Cyclostationarity and The Score Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Blind Adaptive Beamforming for Cyclostationary Signals: A Subspace Projection Approach

Lee

Huang

2009

Antennas Wirel. Propag. Lett.

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In this letter, we present an efficient method for blind adaptive beamforming with cyclostationary signals. In comparison with the SCORE algorithms of Agee et al. (Proc. IEEE, vol. 78, no. 4, pp. 753-767, Apr. 1990), the proposed method achieves better performance than the LS-SCORE algorithm due to using a subspace projection. Moreover, the proposed method requires significantly less computational complexity than the Cross-SCORE algorithm because a fast subspace decomposition algorithm can be utilized. Extension of the proposed method to deal with the situation of multiple signals of interest (SOIs) is also presented. Simulation results for showing the effectiveness of the proposed method are provided.Index Terms-Adaptive beamforming, cyclostationary signals, self-coherent restoral (SCORE), subspace projection.

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Section: B Proposed Subspace Projection Methodsmentioning

confidence: 88%

“…We note from (6), (8), and (9) that , where is a scalar. For any control vector , as long as , it is easy to see from (9) that is equivalent to of (4) when .…”

Section: B Signal Cyclostationarity and The Score Algorithmsmentioning

confidence: 99%

Section: B Signal Cyclostationarity and The Score Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Blind Adaptive Beamforming for Cyclostationary Signals: A Subspace Projection Approach

Lee

Huang

2009

Antennas Wirel. Propag. Lett.

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“…Meanwhile, rapid progress in blind source separation methods [22][23][24][25] has made it possible to develop more efficient models for use in vibration signal classification and recognition. Among these methods, mutual information (MI) analysis can be used to determine the degree of uncertainty associated with the assigned classification category by using several quantified features, such as information entropy [26].…”

Section: Introductionmentioning

confidence: 99%

Three-Level Distributed Real-Time Monitoring of Construction near Underground Infrastructure Using a Combined Intelligent Method

Zhou

Gui

Wang

et al. 2022

Sensors

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With the rapid development of underground infrastructure and the uncertainty of its location, the possibility of damage due to nearby construction has increased. Thus, for the early warning of dangerous construction behaviors around underground facilities, this paper proposes a novel real-time distributed monitoring method with three levels, comprised of the terminal node, relay node, and server. Corresponding to these three monitoring levels, a vibration-based intelligent solution for recognizing the construction source is presented and compared with the traditional method. First, the blind source separation method was used to separate collected signals into a limited number of monitoring object sources; this helped to minimize the number of required classification categories and reduce the recognition uncertainty caused by signal mixing. Then, the mutual information (MI) method was used to select suitable vibration features, which were used as the input matrix for the resulting intelligent recognition. Finally, the construction behaviors were identified at the server based on returned features. Guided by this method, a sample dataset including pile-driving, train-operation, and environment-vibration signals was constructed and combined with a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The effects of blind source separation and the MI method are discussed in depth in this paper.

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“…Blind source separation (BSS), tackling the estimation of original signals from observations (mixtures) received by a set of sensors, has been widely applied in areas involving radar and sonar, communication [1][2][3], speech signal and biomedical signal processing, etc. In recent years, underdetermined blind source separation (UBSS) has received a great deal of attention [4][5][6][7][8][9], where the number of sensors is less than the number of sources.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the complex‐valued mixing matrix by single‐source‐points detection with less sensors than sources

Shen

Wang

et al. 2011

Trans. Emerg. Telecomms. Techs.

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This paper essentially considers the direction‐of‐arrival (DOA) estimation of far‐field source signals in the underdetermined blind separation, where the mixing matrix is complex‐valued. By distinguishing single‐source‐points (SSPs) from multi‐source‐points in the time‐frequency domain, a novel estimation algorithm is proposed based on the detection of SSPs, where only single source contributes and samples all correspond to one of the mixing column vectors. To further enhance the estimation accuracy, a modified version of the proposed algorithm is also presented, which utilizes the probability density distribution of the already detected SSPs and reselects those reliable SSPs with most likely probabilities. Finally, the mixing matrix as well as the DOAs can be obtained by performing the K‐means clustering algorithm on samples at selected SSPs. The results of numerical simulations validate the efficiency of both the two estimation algorithms. One of the outstanding superiorities for the SSPs‐based idea is that it can distinguish multiple DOAs using only two sensors; the other is that it relaxes the signal sparsity requirement to allow SSPs to merely occur at a small number of time‐frequency locations. Copyright © 2011 John Wiley & Sons, Ltd.

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Multiuser processing using blind source separation methods

Cited by 4 publications

References 15 publications

Blind Adaptive Beamforming for Cyclostationary Signals: A Subspace Projection Approach

Blind Adaptive Beamforming for Cyclostationary Signals: A Subspace Projection Approach

Three-Level Distributed Real-Time Monitoring of Construction near Underground Infrastructure Using a Combined Intelligent Method

Estimation of the complex‐valued mixing matrix by single‐source‐points detection with less sensors than sources

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