Name
Title | ||
|---|---|---|
Improving signal subspace estimation and source number detection in the context of spatially correlated noises |
Abstract | ||
|---|---|---|
This paper addresses the issue of Orthogonal Techniques for Blind Source Separation of periodic signals when the mixtures are corrupted with spatially correlated noises. The noise covariance matrix is assumed to be unknown. This problem is of major interest with experimental signals. We first remind that Principal Components Analysis (PCA) cannot provide a correct estimate of the signal subspace in this situation. We then decide to compute the spectral matrices using delayed blocks to eliminate the noise influence. We show that two of these delayed spectral matrices are enough to get the unnoisy spectral matrix. We also introduce a new source number detector which exploits the eigenvectors of a delayed matrix to estimate the signal subspace dimension. Simulation results show that the signal subspace estimation is improved and the source number detector is more efficient in this situation than the usual AIC and MDL criteria. |
Year | Venue | Keywords |
|---|---|---|
1998 | EUSIPCO | blind source separation,eigenvalues and eigenfunctions,principal component analysis,signal denoising,signal detection,pca,delayed matrix eigenvectors,noise covariance matrix,orthogonal technique,periodic signal blind source separation,principal components analysis,signal subspace dimension estimation improvement,source number detection,spatially correlated noise elimination,unnoisy spectral matrix,matrix decomposition,noise,detectors,estimation |
Field | DocType | ISBN |
Pattern recognition,Matrix (mathematics),Matrix decomposition,Artificial intelligence,Covariance matrix,Signal subspace,Blind signal separation,Detector,Eigenvalues and eigenvectors,Mathematics,Principal component analysis | Conference | 978-960-7620-06-4 |
Citations | PageRank | References |
1 | 0.51 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabry, P. | 1 | 1 | 0.51 |
| Christine Servière | 2 | 223 | 17.39 |
| Jean-Louis Lacoume | 3 | 62 | 12.13 |