Generalized Schur Parametrization and Orthogonal Modeling Algorithms for Second-Order Time-Series

被引：0

作者：

Libal, Urszula ^{[1
]}

Magiera, Wladyslaw ^{[1
]}

Wielgus, Agnieszka ^{[1
]}

机构：

[1] Wroclaw Univ Sci & Technol, Fac Elect, Signal Proc Syst Dept, Wyb Wyspianskiego 27, PL-50370 Wroclaw, Poland

来源：

2017 SIGNAL PROCESSING SYMPOSIUM (SPSYMPO) | 2017年

关键词：

Schur parametrization; second-order non-stationary stochastic processes; stochastic modeling;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We present algorithms for orthogonal Schur parametrization, innovations transformation and stochastic modeling of second-order non-stationary time-series, showing statistical equivalence (in a weak second-order sense) of the parametrized and modeled time-series.

引用

页数：6

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