Blind source separation: new tools for extraction of source signals and denoising

2005 ◽  
Author(s):  
Andrzej S. Cichocki
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Source Signals

Blind Source Separation for Under-Determined Mixtures Based on Time-Frequency Analysis

2016 ◽  
Vol 693 ◽  
pp. 1350-1356 ◽  
Author(s):  
Hong Kun Li ◽  
Hong Yi Liu ◽  
Chang Bo He
Keyword(s):  
Blind Source Separation ◽  
Frequency Analysis ◽  
Source Separation ◽  
Vibration Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Diagnosis And Classification ◽  
Source Signals

Blind source separation (BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. But BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. In this research, we provide an under-determined BSS method based on the advantage of time-frequency analysis and empirical mode decomposition (EMD). It is suitable for weak feature extraction and pattern recognition. Firstly, vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are selected according to the EMD. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, BSS based on time-frequency analysis are used to the constructed signals. Gearbox signals are used to verify the effectiveness of this method.

Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems

2019 ◽  
Vol 25 (16) ◽  
pp. 2246-2260 ◽  
Author(s):  
Jiantao Lu ◽  
Wei Cheng ◽  
Yapeng Chu ◽  
Yanyang Zi
Keyword(s):  
Particle Swarm Optimization ◽  
Case Studies ◽  
Blind Source Separation ◽  
Augmented Lagrangian ◽  
Source Separation ◽  
Particle Swarm ◽  
Lagrangian Multiplier ◽  
Lagrangian Particle ◽  
Swarm Optimization ◽  
Source Signals

To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO). First, an improved contrast function is presented by combining mutual information of the separated signals and kurtosis ranges of source signals. Second, an augmented Lagrangian multiplier method is used to convert PNLBSS into an unconstrained pseudo-objective optimization problem. Then, improved PSO is applied to update the parameters in complex nonlinear spaces. Finally, numerical case studies and experimental case studies are provided to evaluate the performance of the proposed method. By adding the kurtosis ranges constraints, the estimation accuracy of source signals could be improved, which would benefit vibration and acoustic monitoring and control.

Multiset Canonical Correlation Analysis Using for Blind Source Separation

2012 ◽  
Vol 195-196 ◽  
pp. 104-108 ◽  
Author(s):  
Hua Gang Yu ◽  
Gao Ming Huang ◽  
Jun Gao
Keyword(s):  
Correlation Analysis ◽  
Blind Source Separation ◽  
Canonical Correlation Analysis ◽  
Statistical Characteristic ◽  
Canonical Correlation ◽  
Temporal Structure ◽  
Source Separation ◽  
Simulation Results ◽  
Source Signals ◽  
Novel Algorithm

To solve the problem of blind source separation, a novel algorithm based on multiset canonical correlation analysis is presented by exploiting the different temporal structure of uncorrelated source signals. In contrast to higher order cumulant techniques, this algorithm is based on second order statistical characteristic of observation signals, can blind separate super-Gaussian and sub-Gaussian signals successfully at the same time with relatively light computation burden. Simulation results confirm that the algorithm is efficient and feasible.

A New Method for Angle Estimation in MIMO Radar with Blind Source Separation

2014 ◽  
Vol 543-547 ◽  
pp. 2500-2504 ◽  
Author(s):  
Yu Cai Pang ◽  
Chao Zhu Zhang
Keyword(s):  
Closed Form ◽  
Blind Source Separation ◽  
Source Separation ◽  
Algebraic Method ◽  
Mimo Radar ◽  
New Method ◽  
Relative Advantage ◽  
Joint Direction ◽  
Simulation Results ◽  
Source Signals

A joint direction of departures (DODs) and direction of arrivals (DOAs) estimation for bistatic MIMO radar with an algebraic method for blind source separation (BSS) is presented. The proposed method has the relative advantage of simplicity. The DODs and DOAs of targets can be solved in closed-form and paired automatically. Moreover, BSS techniques enable this approach to recover the source signals from the mixed signals. Simulation results verify the effectiveness of the method.

Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation

2000 ◽  
Vol 12 (6) ◽  
pp. 1463-1484 ◽  
Author(s):  
Shun-ichi Amari ◽  
Tian-Ping Chen ◽  
Andrzej Cichocki
Keyword(s):  
Blind Source Separation ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Source Separation ◽  
Rapid Change ◽  
Nonholonomic Constraints ◽  
Mixing Coefficients ◽  
Linear Mixtures ◽  
Source Signals ◽  
Geometrical Consideration

Independent component analysis or blind source separation extracts independent signals from their linear mixtures without assuming prior knowledge of their mixing coefficients. It is known that the independent signals in the observed mixtures can be successfully extracted except for their order and scales. In order to resolve the indeterminacy of scales, most learning algorithms impose some constraints on the magnitudes of the recovered signals. However, when the source signals are nonstationary and their average magnitudes change rapidly, the constraints force a rapid change in the magnitude of the separating matrix. This is the case with most applications (e.g., speech sounds, electroencephalogram signals). It is known that this causes numerical instability in some cases. In order to resolve this difficulty, this article introduces new nonholonomic constraints in the learning algorithm. This is motivated by the geometrical consideration that the directions of change in the separating matrix should be orthogonal to the equivalence class of separating matrices due to the scaling indeterminacy. These constraints are proved to be nonholonomic, so that the proposed algorithm is able to adapt to rapid or intermittent changes in the magnitudes of the source signals. The proposed algorithm works well even when the number of the sources is overestimated, whereas the existent algorithms do not (assuming the sensor noise is negligibly small), because they amplify the null components not included in the sources. Computer simulations confirm this desirable property.

scholarly journals Distributed Processing of Blind Source Separation

2021 ◽  
Author(s):  
◽  
Seyed Reza Mir Alavi
Keyword(s):  
Sensor Networks ◽  
Computational Complexity ◽  
Blind Source Separation ◽  
Distributed Algorithms ◽  
Sensor Node ◽  
Distributed Processing ◽  
Source Separation ◽  
Arbitrary Graph ◽  
Constrained Problems ◽  
Source Signals

<p>Communication is performed by transmitting signals through a medium. It is common that signals originating from different sources are mixed in the transport medium. The operation of separating source signals without prior information about the sources is referred to as blind source separation (BSS). Blind source separation for wireless sensor networks has recently received attention because of low cost and the easy coverage of large areas. Distributed processing is attractive as it is scalable and consumes low power. Existing distributed BSS algorithms either require a fully connected pattern of connectivity, to ensure the good performance, or require a high computational load at each sensor node, to enhance the scalability. This motivates us to develop distributed BSS algorithms that can be implemented over any arbitrary graph with fully shared computations and with good performance.  This thesis presents three studies on distributed algorithms. The first two studies are on existing distributed algorithms that are used in linearly constrained convex optimization problems, which are common in signal processing and machine learning. The studies are aimed at improving the algorithms in terms of computational complexity, communication cost, processors coordination and scalability. This makes them more suitable for implementation on sensor networks, thus forming a basis for the development of distributed BSS algorithms on sensor networks in our third study.  In the first study, we consider constrained problems in which the constraint includes a weighted sum of all the decision variables. By formulating a constrained dual problem associated to the original constrained problem, we were able to develop a distributed algorithm that can be run both synchronously and asynchronously on any arbitrary graph with lower communication cost than traditional distributed algorithms.  In the second study, we consider constrained problems in which the constraint is separable. By making use of the augmented Lagrangian function and splitting the dual variable (Lagrange multiplier) associated to each partial constraint, we were able to develop a distributed fully asynchronous algorithm with lower computational complexity than traditional distributed algorithms. The simplicity of the algorithm is the consequence of approximating the constraint on the equality of the decoupled dual variables. We also provide a measure of the inaccuracy in such an approximation on the optimal value of the primal objective function. Finally, in the third study, we investigate distributed processing solutions for BSS on sensor networks. We propose two distributed processing schemes for BSS that we refer to as scheme 1 and scheme 2. In scheme 1, each sensor node estimates one specific source signal while in scheme 2, by formulating a consensus optimization problem, each sensor node estimates all source signals in a fully shared computation manner. Our proposed algorithms carry the following features: low computational complexity, low power consumption, low data transmission rate, scalability and excellent performance over arbitrary graphs. Although all of our proposed algorithms share the aforementioned properties, each of them is superior in one or some of the features compared to the others. Comparative experimental results show that among all our proposed distributed BSS algorithms, a variant of scheme 1 performs best when all features are considered. This is achieved by making use of the concept of pairwise mutual information along with adding a sparsity assumption on the parameters of the model that is used in BSS.</p>

scholarly journals Under-Determined Blind Source Separation

2021 ◽  
Author(s):  
Mehrdad Kafaiezadtehrani
Keyword(s):  
Blind Source Separation ◽  
Clustering Algorithm ◽  
Matching Pursuit ◽  
Source Separation ◽  
Least Square ◽  
Separation Problem ◽  
Second Stage ◽  
Mixing Matrix ◽  
Matching Pursuit Algorithm ◽  
Source Signals

The Under-determined Blind Source Separation problem aims at estimating N source signals, with only a given set of M known mixtures, where M < N. The problem is solved by a two-stage approach. The rst stage is the estimation of the unknown mixing matrix. The contributions made unravel a more precise and accurate tool which directly relates to the initialization of the clustering algorithm. Di erent schemes such as segmentation, correlation and least square curve tting are used to take advantage of the sparsity of the sources. A signi cant addition involves applying linear transforms to produce a higher sparse domain. Further, the second stage is the sparse source recovery using a Matching Pursuit algorithm. The contributions involve a Matching Pursuit algorithm with di

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