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.
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页数：6
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