Sample covariance based estimation of Capon algorithm error probabilities

Christ D. Richmond; Robert L. Geddes; Ramis Movassagh; Alan Edelman

doi:10.1109/ACSSC.2010.5757895

Sample covariance based estimation of Capon algorithm error probabilities

Christ D. Richmond, Robert L. Geddes, Ramis Movassagh, Alan Edelman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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	https://doi.org/10.1109/ACSSC.2010.5757895
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

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

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

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2010.5757895

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Richmond, C. D., Geddes, R. L., Movassagh, R., & Edelman, A. (2010). Sample covariance based estimation of Capon algorithm error probabilities. In Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 (pp. 1842-1845). [5757895] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2010.5757895

Sample covariance based estimation of Capon algorithm error probabilities. / Richmond, Christ D.; 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 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Richmond, CD, Geddes, RL, Movassagh, R & Edelman, A 2010, Sample covariance based estimation of Capon algorithm error probabilities. in Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010., 5757895, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 1842-1845, 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010, Pacific Grove, CA, United States, 11/7/10. https://doi.org/10.1109/ACSSC.2010.5757895

Richmond CD, Geddes RL, Movassagh R, Edelman A. Sample covariance based estimation of Capon algorithm error probabilities. In Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010. 2010. p. 1842-1845. 5757895. (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2010.5757895

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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.",

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