The decomposition of vector time series data into orthogonal components can be applied in both temporal and spatial discrete frequency analysis. If the observed multidimensional data is non-stationary, then adaptive procedures can be used for estimation of the eigendata. This paper presents the relationship between multicomponent, spectral signals in noise and the corresponding eigendata. Two adaptive realizations of the eigendata estimation process are considered. Examples are given which allow a comparison between signal detection by data orthogonalization, power spectrum estimation and two channel magnitude squared coherence computation.
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N.L. Owsley (2005) studied this question.
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