Name
Abstract | ||
|---|---|---|
Many subspace-based array signal processing algorithms assume that the noise is spatially white. In this case, the noise covariance matrix is a multiple of the identity and the eigenvectors of the data covariance matrix are not affected by it. If the noise covariance is an unknown arbitrary diagonal (e.g., for an uncalibrated array), the eigenvalue decomposition leads to incorrect subspace estimat... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2017.2780047 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Covariance matrices,Signal processing algorithms,Data models,Computational modeling,Arrays,Approximation algorithms,Mathematical model | Diagonal,Approximation algorithm,Mathematical optimization,Matrix (mathematics),Algorithm,Eigendecomposition of a matrix,Covariance matrix,Eigenvalues and eigenvectors,Mathematics,Block matrix,Covariance | Journal |
Volume | Issue | ISSN |
66 | 4 | 1053-587X |
Citations | PageRank | References |
1 | 0.43 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sardarabadi, Ahmad Mouri | 1 | 6 | 3.33 |
| Veen, Alle-Jan van der | 2 | 4 | 3.64 |