Paper Info
Title
Super-exponential methods for blind deconvolution
Abstract
A class of iterative methods for solving the blind deconvolution problem, i.e. for recovering the input of an unknown possibly nonminimum-phase linear system by observation of its output, is presented. These methods are universal do not require prior knowledge of the input distribution, are computationally efficient and statistically stable, and converge to the desired solution regardless of initialization at a very fast rate. The effects of finite length of the data, finite length of the equalizer, and additive noise in the system on the attainable performance (intersymbol interference) are analyzed. It is shown that in many cases of practical interest the performance of the proposed methods is far superior to linear prediction methods even for minimum phase systems. Recursive and sequential algorithms are also developed, which allow real-time implementation and adaptive equalization of time-varying systems
Year
DOI
Venue
1993
10.1109/18.212280
IEEE Transactions on Information Theory
Keywords
DocType
Volume
linear prediction method,minimum phase system,super-exponential method,input distribution,nonminimum-phase linear system,time-varying system,attainable performance,blind deconvolution problem,additive noise,adaptive equalization,finite length,signal processing,iterative methods,distributed computing,linear systems,real time systems,linear system,information theory,deconvolution,convergence,iteration method,parameter estimation,intersymbol interference,adaptive equalizer,equalizer,blind deconvolution
Journal
39
Issue
ISSN
Citations 
2
0018-9448
100
PageRank 
References 
Authors
32.71
3
2
Name
Order
Citations
PageRank
Ofir Shalvi111334.63
EHUD WEINSTEIN2347114.98