A Multistage Space–Time Equalizer for Blind Source Separation

Moazzen, Iman; Agathoklis, P.

doi:10.1007/s00034-015-0042-4

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…Assume that the number of sources is same as that of sensors in receiver. The noisy mathematical model of observed mixtures is always represented as 2,4–27…”

Section: Rwsdmmentioning

confidence: 99%

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Robust wireless statistic division multiplexing and its performance analysis

Luo

2018

International Journal of Distributed Sensor Networks

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Aiming at solving the issue of the robustness of sensor signal recovery as well as tackling the dilemma of simultaneous spectrum utilization, this article will concentrate on designing a robust wireless statistic division multiplexing scheme. The proposed robust wireless statistic division multiplexing transmits multiple sensor signals and shares the same frequency band simultaneously. The fundamental principle of robust wireless statistic division multiplexing is described as statistical independence or statistical distinction of the sensor source signals. An unsupervised learning mechanism named independent component analysis is used for robust wireless statistic division multiplexing receiving processing. The robust wireless statistic division multiplexing can not only contribute tremendously to enhancing the spectrum efficiency but also bring in attractive anti-noise performance. In this article, first, the fundamental mechanism of robust wireless statistic division multiplexing will be discussed in comparison with that of time division multiplexing, frequency division multiplexing and code division multiplexing. Second, in terms of robust wireless statistic division multiplexing receiving mechanism based on independent component analysis principle, a novel cost function based on minimization mutual information modified by minimum bit error rate constraint is investigated to improve robust performance of source recovery. Furthermore, independent component analysis-based blind source separation method combines with an adaptive moment estimation stochastic optimization for promoting the effectiveness of the source recovery performance. Finally, theoretical analysis and simulation experiments demonstrate the effectiveness and robustness performance of robust wireless statistic division multiplexing.

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“…Assume that the number of sources is same as that of sensors in receiver. The noisy mathematical model of observed mixtures is always represented as 2,4–27…”

Section: Rwsdmmentioning

confidence: 99%

“…With regard to the receiver in WSDM, the source signals are retrieved by blind source separation (BSS) based on independent component analysis (ICA, such as Fastica and Infomax). 1–23 However, the conventional ICA model does not take into account the influence of noise, and the robustness of performance is weak.…”

Section: Introductionmentioning

confidence: 99%

Robust wireless statistic division multiplexing and its performance analysis

Luo

2018

International Journal of Distributed Sensor Networks

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“…As human activities and science develop rapidly, there is a growing demand for the systems which can automatically change their parameters and performances in response to environmental and functional changes. Numerous studies have been conducted on the time‐varying system modeling problem in order to adapt to environmental disturbances and new requirements 13‐15 . The Kalman filtering technique is generally utilized for the non‐stationary signal enhancement based on the output correlation method 16 .…”

Section: Introductionmentioning

confidence: 99%

“…requirements. [13][14][15] The Kalman filtering technique is generally utilized for the non-stationary signal enhancement based on the output correlation method. 16 A typical distributed time-varying convex optimization problem with inequality constraints was discussed for a neurodynamic system.…”

mentioning

confidence: 99%

Parameter estimation for a class of time‐varying systems with the invariant matrix

Ding

2022

Intl J Robust & Nonlinear

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This article is concerned with the identification of time-varying systems. Differently from the conventional polynomial approximation approaches, the changing laws of the time-varying parameters are considered to build the identification model for the time-varying systems. Specifically, the concept of the invariant matrix is put forward to characterize the time-varying parameters and to establish the state-space model with regard to the system parameters. Then this article proposes a stacked state estimation algorithm to achieve the time-varying parameter estimation. Moreover, for the purpose of enhancing the computational efficiency, a detached state estimation algorithm is proposed by reducing the dimension of the state vector to reconstruct the state equation. Finally, a numerical simulation example is employed to demonstrate the effectiveness of the proposed algorithms.

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“…The only assumption is that the transmitted signals satisfy the constant modulus property. In the work [ 12 ], a multistage space–time equalizer is proposed to blindly separate signals received by an antenna array from different sources simultaneously. In the algorithm, each stage consists of an adaptive beamformer, a DOA estimator and an equalizer which are jointly optimized using the constant modulus property of the sources.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution †

Woo

Gao

Bouridane

et al. 2018

Sensors

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This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization–minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time–frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy.

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A Multistage Space–Time Equalizer for Blind Source Separation

Cited by 3 publications

References 22 publications

Robust wireless statistic division multiplexing and its performance analysis

Robust wireless statistic division multiplexing and its performance analysis

Parameter estimation for a class of time‐varying systems with the invariant matrix

Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution †

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