In this work, we will confront the problem of source separation in the field of astrophysics, where the contributions of various Galactic and extra-Galactic components need to be separated from a set of observed noisy mixtures. Most of the previous work on the problem perform blind source separation, assume noiseless models, and in the few cases when noise is taken into account assume Gaussianity and spaceinvariance. However, in the real scenario both the sources and the noise are space-varying. In this work, we present a novel technique, namely particle filtering, for the non-blind (Bayesian) solution of the source separation problem, in case of non-stationary sources and noise, by exploiting available a-priori information.
|Number of pages||8|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2004|
ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science