Eigenvector Algorithms Incorporated With Reference Systems for Solving Blind Deconvolution of MIMO-IIR Linear Systems

Kawamoto, Mitsuru; Kohno, Kiyotaka; Inouye, Yasushi

doi:10.1109/lsp.2007.906225

Cited by 17 publications

(38 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are particularly appealing because the corresponding maximization problem is quadratic with respect to the searched parameters. Taking advantage of this quadratic feature, a maximization algorithm based on singular value decomposition (SVD) has been proposed [11], [13] and was shown to be significantly quicker than other maximization algorithms. In the case where the "reference signal" is close to an actual source, the SVD based method is very efficient for the extraction of this source.…”

Section: A Generalitiesmentioning

confidence: 99%

See 1 more Smart Citation

New Kurtosis Optimization Schemes for MISO Equalization

Castella

Moreau

2012

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-This paper deals with efficient optimization of cumulant based contrast functions. Such a problem occurs in the blind source separation framework, where contrast functions are criteria to be maximized in order to retrieve the sources. More precisely, we focus on the extraction of one source signal and our method applies in deflation approaches, where the sources are extracted one by one.We propose new methods to maximize the kurtosis contrast function. These methods are intermediate between a gradient and an iterative "fixed-point" optimization of so-called reference contrasts. They rely on iterative updates of the parameters which monotonically increase the contrast function value: we point out the strong similarity with the EM (Expectation-Maximization) method and with recent generalizations referred to as MM (Minimization-Maximization). We also prove the global convergence of the algorithm to a stationary point. Simulations confirm the convergence of our methods to a separating solution. They also show experimentally that our methods have a much lower computational cost than former classical optimization methods. Finally, simulations suggest that the methods remain valid under weaker conditions than those required for proving convergence.

show abstract

Section: A Generalitiesmentioning

confidence: 99%

“…Remind the definition of w, v in Equation (13) and the definition of x(n) in Equation (14). It is then straightforward to see that the processed output and the "reference" signal defined in Equations (3) and (4) can be written as:…”

Section: Appendix C Criterion Derivative and Optimal Stepmentioning

confidence: 99%

New Kurtosis Optimization Schemes for MISO Equalization

Castella

Moreau

2012

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…More precisely, V updates following W in each loop iteration step. Then the separation quality of the algorithm in [10] is better than that in [11]. In [14] and [15], we have done some corresponding work to investigate the impact of reference signals, which is similar to [12].…”

Section: Reference Signalsmentioning

confidence: 99%

An Efficient Algorithm by Kurtosis Maximization in Reference-Based Framework

Zhao¹,

Wei²,

Shen³

et al. 2015

RADIOENGINEERING

View full text Add to dashboard Cite

Abstract. This paper deals with the optimization of kurtosis for complex-valued signals in the independent component analysis (ICA) framework, where source signals are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast schemes, a similar contrast function is put forward, based on which a new fast fixed-point (FastICA) algorithm is proposed. The new optimization method is similar in spirit to the former classical kurtosis-based FastICA algorithm but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. The performance of this new algorithm is confirmed through computer simulations.

show abstract

“…For the classical FastICA algorithm in [1], we choose J in (10) as the contrast function with nonlinear functions G1, G2 and G3 in (12) to (14). The corresponding algorithm is denoted by G 1 , G 2 and G 3 for different choices of nonlinear functions G1, G2, and G3, respectively.…”

Section: Performance Analysismentioning

confidence: 99%

A Novel Method for Complex-Valued Signals in Independent Component Analysis Framework

Zhao

Shen

Zeng

et al. 2014

Circuits Syst Signal Process

View full text Add to dashboard Cite

This paper deals with the separation problem of complex-valued signals in the independent component analysis (ICA) framework, where sources are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast criteria based on kurtosis, a new contrast function is put forward by introducing the reference-based scheme to negentropy, based on which a novel fast fixed-point (FastICA) algorithm is proposed. This method is similar in spirit to the classical negentropy-based FastICA algorithm, but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. Furthermore, compared with the kurtosis-based

show abstract

Eigenvector Algorithms Incorporated With Reference Systems for Solving Blind Deconvolution of MIMO-IIR Linear Systems

Cited by 17 publications

References 14 publications

New Kurtosis Optimization Schemes for MISO Equalization

New Kurtosis Optimization Schemes for MISO Equalization

An Efficient Algorithm by Kurtosis Maximization in Reference-Based Framework

A Novel Method for Complex-Valued Signals in Independent Component Analysis Framework

Contact Info

Product

Resources

About