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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1783

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dc.contributor.authorChakraborty, M-
dc.contributor.authorPrasad, S-
dc.identifier.citationSignal Processing, IEEE Transactions on, 41(4), 1692 - 1697p.en
dc.description.abstractAn algorithm for multichannel autoregressive moving average (ARMA) modeling which uses scalar computations only and is well suited for parallel implementation is proposed. The given ARMA process is converted to an equivalent scalar, periodic ARMA process. The scalar autoregressive (AR) parameters are estimated by first deriving a set of modified Yule-Walker-type equations and then solving them by a parallel, order recursive algorithm. The moving average (MA) parameters are estimated by a least squares method from the estimates of the input samples obtained via a high-order, periodic AR approximation of the scalar processen
dc.format.extent78207 bytes-
dc.subjectmultichannel autoregressiveen
dc.subjectperiodic ARMA processen
dc.subjectscalar autoregressive (AR) parametersen
dc.subjectmoving average (MA) parametersen
dc.titleMultivariate ARMA modeling by scalar algorithmsen
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