Weighted least-squares blind deconvolution of non-minimum phase systems

Trans Emerging Tel Tech

2002

Section: U2 = A'(t) As It Is a Function Of W(t)mentioning

confidence: 99%

“…A different cost function was introduced by the present author in [4], which gives rise to a weighted least-squares (WLS) blind deconvolution theory. It reads:…”

Section: Cost Functions and Estimatorsmentioning

confidence: 99%

See 1 more Smart Citation

Notes on cost functions and estimators for ‘Bussgang’ adaptive blind equalization

Trans Emerging Tel Tech

2002

“…We have recently surveyed and developed sophisticated techniques that allow designing proper adapting criteria under the form of objective functions of filter parameters to be optimized [2,3]; in the present context we only pay attention to the structure stability problem, thus we employ the well-known minimumentropy deconvolution criterion, based on filter response kurtosis [8,7]. Under the hypothesis that the source signal exciting the channel to deconvolve has bounded power, and the channel itself has finite-energy impulse response, the one of such barrier terms is log ρ α , with 0 < α ≪ 1.…”

Section: Stable Two-pole Filter Structure and Adaptingmentioning

confidence: 99%

“…Many engineering applications rely on blind deconvolution, such as channel equalization in telecommunications, blind deblurring in digital image restoration, remote sensing in geoscience, and data storage by electro-optic devices; for a recent review of these applications see e.g. [1,2,3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Blind intrinsically-stable 2-pole IIR filtering

2002

Electron. Lett.

Self Cite

The aim of this Letter is to present a preliminary study on intrinsicallystable discrete-time 2-pole IIR adaptive filtering for blind equalization based on minimum-entropy deconvolution. The structural-adapting theory is developed and numerical experimental results are discussed which illustrate the soundness of the proposed theory: The adaptive filter keeps stable and good deconvolution results are obtained.

Topics in Blind Signal Processing by Neural Networks

Perspectives in Neural Computing

2002

The aim of this paper is to propose a brief summary of the research topics and results presented in the Author's PhD Thesis, concerning blind signal processing by artificial neural networks. The results are illustrated by the citation of the formal instruments involved in the research activity and by several links to the main published contributions. Introd uctionSeveral signal processing problems are currently tackled by means of linear and non-linear discrete-time adaptive circuits, which are capable of self-designing in order to achieve a pre-defined target.Such structures have raised an ever increasing interest in the international scientific community as they allow for solving a number of problems which turn out to be incompatible with the classical solutions; in fact, they allow to easily manage the uncertainty inherent in any design activity. Furthermore, they permit to achieve the desired results even in the partial, or sometimes total, lack of fundamental information about the statistical and temporal features of the signals under analysis, about the transmission means that the signals propagate within, and about the receivers and measurement devices used as sensors and transducers.Owing to their noticeable flexibility and complexity and their relative novelty, such adaptive structures, linear as well as non-linear, are not endowed with a complete and consistent theory which would allow their synthesis and analysis under general conditions: for this reason they appear in the literature with configurations still not completely satisfactory about their capability of achieving the desired targets, and rather inefficient about the computational and structural complexity required in order to achieve these targets. It is thus