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- W80428706 abstract "This chapter reviews the increasing attention that subspace methods have received in signal processing and control in recent years, due to their successful application to the problems of multivariable system identification and source localization. This chapter gives a tutorial overview of these two applications, in order to draw out features common to both problems. Both problems are based on finding spanning vectors for the null space of a spectral density matrix that characterizes the available data. This is expressed numerically in various formulations, usually in terms of external singular vectors of a data matrix, or in terms of orthogonal filters, which achieve decorrelation properties of filtered data sequences. In view of this algebraic similarity, algorithms designed for one problem may be adapted to the other. However, in both cases, successful application of subspace methods depends on some knowledge of the required filter order of spanning vectors for the desired null space. Data encountered in real applications rarely give rise to finite order filters, if theoretically “exact” subspace fits are desired. Accordingly, some observations on the performance of subspace methods in reduced order cases are developed." @default.
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- W80428706 date "1995-01-01" @default.
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- W80428706 title "Subspace Methods in System Identification and Source Localization" @default.
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- W80428706 doi "https://doi.org/10.1016/b978-044482106-5/50002-8" @default.
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