NDLI: Improving and characterizing the performance of stochastic matched subspace detectors when using noisy estimated subspaces

Content Provider	IEEE Xplore Digital Library
Author	Asendorf, N. Nadakuditi, R.R.
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, 48105, USA (Asendorf, N.; Nadakuditi, R.R.)
Abstract	We consider a stochastic matched subspace detection problem where the signal subspace is unknown and estimated by taking the eigenvalue decomposition of the sample covariance matrix of noisy signal-bearing training data. In moderate to low signal-to-noise ratio (SNR) regimes or in the setting where the number of samples is limited, subspace estimation errors affect the performance of matched subspace detectors. We use random matrix theory to derive an optimal matched subspace detector which accounts for these estimation errors and to analytically predict the associated ROC performance curves. What emerges from the analysis is the importance of using only the k ≤ k informative signal subspace components that can be reliably estimated from the noisy, limited data. Specifically, the ROC analysis shows that the performance of the optimal detector matches that of the plug-in detector that uses exactly k components. The analytical predictions are validated using numerical simulations.
Starting Page	1893
Ending Page	1897
File Size	167922
Page Count	5
File Format	PDF
ISBN	9781467303217
ISSN	10586393
e-ISBN	9781467303231
DOI	10.1109/ACSSC.2011.6190352
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Eigenvalues and eigenfunctions Covariance matrix Noise measurement Approximation methods Accuracy Estimation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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