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 proceedingConference 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 languageEnglish (US)
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages1842-1845
Number of pages4
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

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

Other

Other44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
CountryUnited States
CityPacific Grove, CA
Period11/7/1011/10/10

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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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