Simon P. Wilson, Ercan E. Kuruoglu and Emanuele Salerno ‘Fully Bayesian source separation with application to the cosmic microwave background’ in IEEE Journal of Selected Topics in Signal Processing: special issue on Signal Processing for Astronomical and Space Research Applications, 2, (5), 2008, pp 685 - 696
IEEE Journal of Selected Topics in Signal Processing: special issue on Signal Processing for Astronomical and Space Research Applications 2 5
We address the problem of source separation in the
presence of prior information. We develop a fully Bayesian source
separation technique that assumes a very flexible model for the
sources, namely the Gaussian mixture model with an unknown
number of factors, and utilize Markov chain Monte Carlo techniques
for model parameter estimation. The development of this
methodology is motivated by the need to bring an efficient solution
to the separation of components in the microwave radiation
maps to be obtained by the satellite mission Planck which has the
objective of uncovering cosmic microwave background radiation.
The proposed algorithm successfully incorporates a rich variety of
prior information available to us in this problem in contrast to most
of the previous work which assumes completely blind separation of
the sources. We report results on realistic simulations of expected
Planck maps and on WMAP 5th year results. The technique suggested
is easily applicable to other source separation applications
by modifying some of the priors.
Index Terms—Bayesian source separation, cosmic microwave
background (CMB) radiation, Gibbs sampling, Markov chain
Monte Carlo, Planck satellite mission.
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