Sample covariance based estimation of Capon algorithm error probabilities
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Edelman_Sample Covariance.pdf
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682.96 KB
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Author(s)
Richmond, Christ D.
Movassagh, Ramis
Movassagh, Ramis
Edelman, Alan
Date Issued
April 2011
Journal
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Richmond, Christ D. et al. “Sample Covariance Based Estimation of Capon Algorithm Error Probabilities.” IEEE, 2010. 1842–1845. Web. 11 Apr. 2012. © 2011 Institute of Electrical and Electronics Engineers
Version
Final published version
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.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Mathematics
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ACSSC.2010.5757895
