作者：
G. MengaC. GrecoCENS
Istituto di Elettrotecnica Generale Politecnico di Torino Torino Italy
This paper presents a package for modeling and identification of linear multivariable systems from only the knowledge of input - output data. The method, that is original, belongs to the least squares family and appli...
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This paper presents a package for modeling and identification of linear multivariable systems from only the knowledge of input - output data. The method, that is original, belongs to the least squares family and applies new efficient factorization algorithms drawn from the numerical linear algebra literature. Performances of the package and comparisons with other methods on a simulated real size power plant are illustrated.
Recently the research in the area of search and rescue satellite aided tracking (SARSAT) has gained great impetus. The efficiency of the search and rescue (SAR) program depends, amongst other factors, on the accuracy ...
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Recently the research in the area of search and rescue satellite aided tracking (SARSAT) has gained great impetus. The efficiency of the search and rescue (SAR) program depends, amongst other factors, on the accuracy of the position information of the distressed platform, e.g. a downed aircraft. This in turn depends on the accuracy of the estimate of the SARSAT satellite's orbital position. In the present work we utilize extended Kalman Kilter (EKF) and UD-Kilters (UDF) to estimate the orbital position of a SARSAT satellite. We study the effect of two sets of observables and a certain a priori statistics on the position and velocity estimates. Altman's Unified State Model (USM) is used for the orbital trajectory dynamics and angles and range are used as observables. The results of the comparison of four orbit estimators are presented for simulated data obtainable from a ground based observation site.
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