Optimal Multichannel Blind Deconvolution for
Parameterized Channels and Known Source Densities (EDICS:SSP-SSEP)
June 2005
Robert M. Taylor, Jr., The MITRE Corporation
L. Mili, Electrical and Computer Engineering Department, Alexandria Research Institute of Virginia Tech
Amir I. Zaghloul, Electrical and Computer Engineering Department, Alexandria Research Institute of Virginia Tech
ABSTRACT
In this study we consider the problem of how
to design an optimal multichannel blind deconvolution (MBD)
algorithm in the case where the probability density functions
of the source signals are known. We assume existence of a
parametric channel model that accurately characterizes the
propagation environment. Through three major steps we derive a
blind channel parameter estimator that is used to jointly compute
the separation system and recover all the source signals. First, we
replace the normally assumed nonparametric channel model with
a physical model. Next, we introduce a symbolic pseudoinverse
for our separation model to replace the ubiquitous inverse filter
separation model. Thirdly, we introduce a minimum divergence
estimator formulation to replace the commonly used minimum
entropy formulation. We prove that the new estimator formed
in this way is asymptotically consistent and Fisher-efficient.
Through simulation we show the superior performance of our
algorithm compared with existing techniques based on entropy
minimization and inverse filter separation.
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optimal multichannel blind deconvolution, blind
source separation, parametric channel model, known source
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