Blind adaptation of stable discrete-time IIR filters in state-space form

Fiori, Simone

doi:10.1109/tsp.2006.874807

Cited by 11 publications

(1 citation statement)

References 21 publications

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“…Deep zeros in a system impulse response would make it necessary to adopt a deconvolving filter whose impulse response should be very long, which would make FIR filtering unsuitable. In this case, infinite impulse response filtering would be appropriate, as proposed in [24,25]. Provided that zeros close to the unit circle are excluded, the system's zeros may lie both inside and outside the unit circle, meaning that minimum-phase as well as non-minimum phase systems deconvolution may be tackled within the Bussgang framework.…”

Section: Introductionmentioning

confidence: 99%

Blind deconvolution by a Newton method on the non‐unitary hypersphere

Fiori

2012

Adaptive Control & Signal

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Blind deconvolution is an inverse filtering technique that has received increasing attention from academia as well as industry because of its theoretical implications and practical applications, such as in speech dereverberation, nondestructive testing and seismic exploration. An effective blind deconvolution technique is known as 'Bussgang', which relies on the iterative Bayesian estimation of the source sequence. Automatic gain control in blind deconvolution keeps constant the energy of the inverse filter impulse response and controls the magnitude of the estimated source sequence. The aim of the present paper is to introduce a class of Newton-type algorithms to optimize the Bussgang cost function on the inverse-filter parameter space whose geometrical structure is induced by the automatic-gain-control constraint. As the parameter space is a differentiable manifold, the Newton-like optimization method is formulated in terms of differential geometrical concepts. The present paper also discusses convergence issues related to the introduced Newton-type optimization algorithms and illustrates their performance on a comparative basis.

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Section: Introductionmentioning

confidence: 99%

Blind deconvolution by a Newton method on the non‐unitary hypersphere

Fiori

2012

Adaptive Control & Signal

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Novel fast random search clustering algorithm for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs

2012

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In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman-Pearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios.

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Geodesic-based and projection-based neural blind deconvolution algorithms

Fiori¹

2008

Signal Processing

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Blind adaptation of stable discrete-time IIR filters in state-space form

Cited by 11 publications

References 21 publications

Blind deconvolution by a Newton method on the non‐unitary hypersphere

Blind deconvolution by a Newton method on the non‐unitary hypersphere

Novel fast random search clustering algorithm for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs

Geodesic-based and projection-based neural blind deconvolution algorithms

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