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AR models of singular spectral matrices

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Anderson, Brian
Deistler, Manfred
Chen, Weitian
Filler, Alexander

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Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

This paper deals with autoregressive models of singular spectra. The starting point is the assumption that there is available a transfer function matrix W(q) expressible in the form D -1(q)B for some tall constant matrix B of full column rank and with the determinantal zeros of D(q) all stable. It is shown that, even if this matrix fraction representation of W(q) is not coprime, W(q) has a coprime matrix fraction description of the form D̃ -1(q)[I m 0] T . It is also shown how to characterize the equivalence class of all autoregressive matrix fraction descriptions of W(q), and how canonical representatives can be obtained. A canonical representative can be obtained with a minimal set of row degrees for the submatrix of D̃(q) obtained by deleting the first m rows. The paper also considers singular autoregressive descriptions of nested sequences of W p(q), p = p 0, p 0+1, . . . , where p denotes the number of rows, and shows that these canonical descriptions are nested, and contain a number of parameters growing linearly with p.

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Proceedings of IEEE Conference on Decision and Control and Chinese Control Conference 2009

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2037-12-31