Adaptive blind deconvolution of linear channels using Renyi's entropy with Parzen window estimation

Blind deconvolution of linear channels is a fundamental signal processing problem that has immediate extensions to multiple-channel applications. In this paper, we investigate the suitability of a class of Parzen-window-based entropy estimates, namely Renyi's entropy, as a criterion for blind d...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2004-06, Vol.52 (6), p.1489-1498
Main Authors: Erdogmus, D., Hild, K.E., Principe, J.C., Lazaro, M., Santamaria, I.
Format: Article
Language:English
Subjects:
Applied sciences
Blinds
Channels
Computer simulation
Deconvolution
Density
Detection, estimation, filtering, equalization, prediction
Entropy
Equalizers
Exact sciences and technology
Independent component analysis
Information, signal and communications theory
Length measurement
Monte Carlo methods
Nonlinear filters
Principal component analysis
Signal and communications theory
Signal processing
Signal processing algorithms
Signal, noise
Source separation
Telecommunications and information theory
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Summary:Blind deconvolution of linear channels is a fundamental signal processing problem that has immediate extensions to multiple-channel applications. In this paper, we investigate the suitability of a class of Parzen-window-based entropy estimates, namely Renyi's entropy, as a criterion for blind deconvolution of linear channels. Comparisons between maximum and minimum entropy approaches, as well as the effect of entropy order, equalizer length, sample size, and measurement noise on performance, will be investigated through Monte Carlo simulations. The results indicate that this nonparametric entropy estimation approach outperforms the standard Bell-Sejnowski and normalized kurtosis algorithms in blind deconvolution. In addition, the solutions using Shannon's entropy were not optimal either for super- or sub-Gaussian source densities.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2004.827202