Abstract
The method of interval estimation (MIE) provides a strategy for mean squared error (MSE) prediction of algorithm performance at low signal-to-noise ratios (SNR) below estimation threshold where asymptotic predictions fail. MIE interval error probabilities for the Capon algorithm are known and depend on the true data covariance and assumed signal array response. Herein estimation of these error probabilities is considered to improve representative measurement errors for parameter estimates obtained in low SNR scenarios, as this may improve overall target tracking performance. A statistical analysis of Capon error probability estimation based on the data sample covariance matrix is explored herein.
| Original language | English (US) |
|---|---|
| Title of host publication | Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 |
| Pages | 1842-1845 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Dec 1 2010 |
| Externally published | Yes |
| Event | 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States Duration: Nov 7 2010 → Nov 10 2010 |
Other
| Other | 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 |
|---|---|
| Country | United States |
| City | Pacific Grove, CA |
| Period | 11/7/10 → 11/10/10 |
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ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications
Cite this
Sample covariance based estimation of Capon algorithm error probabilities. / Richmond, Christ; Geddes, Robert L.; Movassagh, Ramis; Edelman, Alan.
Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010. 2010. p. 1842-1845 5757895.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Sample covariance based estimation of Capon algorithm error probabilities
AU - Richmond, Christ
AU - Geddes, Robert L.
AU - Movassagh, Ramis
AU - Edelman, Alan
PY - 2010/12/1
Y1 - 2010/12/1
N2 - The method of interval estimation (MIE) provides a strategy for mean squared error (MSE) prediction of algorithm performance at low signal-to-noise ratios (SNR) below estimation threshold where asymptotic predictions fail. MIE interval error probabilities for the Capon algorithm are known and depend on the true data covariance and assumed signal array response. Herein estimation of these error probabilities is considered to improve representative measurement errors for parameter estimates obtained in low SNR scenarios, as this may improve overall target tracking performance. A statistical analysis of Capon error probability estimation based on the data sample covariance matrix is explored herein.
AB - The method of interval estimation (MIE) provides a strategy for mean squared error (MSE) prediction of algorithm performance at low signal-to-noise ratios (SNR) below estimation threshold where asymptotic predictions fail. MIE interval error probabilities for the Capon algorithm are known and depend on the true data covariance and assumed signal array response. Herein estimation of these error probabilities is considered to improve representative measurement errors for parameter estimates obtained in low SNR scenarios, as this may improve overall target tracking performance. A statistical analysis of Capon error probability estimation based on the data sample covariance matrix is explored herein.
UR - http://www.scopus.com/inward/record.url?scp=79958014048&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79958014048&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2010.5757895
DO - 10.1109/ACSSC.2010.5757895
M3 - Conference contribution
SN - 9781424497218
SP - 1842
EP - 1845
BT - Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
ER -