Name
Abstract | ||
|---|---|---|
Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to previously explored perturbation of the analytic eigenvalues and eigenspaces of a parahermitian cross-spectral density matrix when estimated from finite data. |
Year | Venue | Keywords |
|---|---|---|
2019 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | Space-time covariance, estimation, parahermitian matrix EVD, polynomial matrices |
Field | DocType | ISSN |
Applied mathematics,Mathematical optimization,Computer science,Covariance matrix,Sample space,Density matrix,Perturbation (astronomy),Eigenvalues and eigenvectors,Estimator | Conference | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Connor Delaosa | 1 | 0 | 0.34 |
| Jennifer Pestana | 2 | 37 | 9.93 |
| Nicholas J. Goddard | 3 | 0 | 0.34 |
| Samuel Dilshan Somasundaram | 4 | 47 | 4.66 |
| Weiss, Stephan | 5 | 209 | 33.25 |