Proceedings Article10.1109/CDC.1980.271941
Data-adaptive principal component signal processing
Ramdas Kumaresan,Donald W. Tufts +1 more
- 01 Dec 1980
- Vol. 19, pp 949-954
56
TL;DR: In this paper, principal eigenvalues and eigenvectors of a sample correlation matrix are used to improve the signal to noise ratio (SNR) in the data and to increase the resolution capability of nonlinear least squares at low SNR and linear prediction based frequency estimation methods.
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Abstract: Principal component (eigenvalue-eigenvector) analysis is applied to processing of narrow band signals in noise. The amount of data available is assumed to be limited. Principal eigenvalues and eigenvectors of a sample correlation matrix are used to improve the signal to noise ratio (SNR) in the data and to increase the resolution capability of nonlinear least squares at low SNR and linear prediction based frequency estimation methods. Relation to Pronylike methods is explored. Performance of different methods is compared experimentally among themselves and to the Cramer-Rao (CR) bound.
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An Introduction to Multivariate Statistical Analysis
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TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
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