Filtering in Stochastic Systems: Analysis for the case of continuous observations with memory of arbitrary multiplicity

Dades bibliogràfiques
Parent link:Journal of Physics: Conference Series
Vol. 803 : Information Technologies in Business and Industry (ITBI2016).— 2017.— [012129, 8 p.]
Autor corporatiu: Национальный исследовательский Томский политехнический университет (ТПУ) Физико-технический институт (ФТИ) Кафедра высшей математики и математической физики (ВММФ), Национальный исследовательский Томский политехнический университет (ТПУ) Институт кибернетики (ИК)
Altres autors: Rozhkova O. V. Olga Vladimirovna, Demin N. S., Li J., Moldovanova E. A. Evgeniya Aleksandrovna, Natalinova N. M. Nataliya Mikhailovna, Genkel V. A.
Sumari:Title screen
We consider stochastic systems with continuous time over observations with memory in the presence of an anomalous noise. The paper is devoted to analysis of some properties of an optimal unbiased in mean-square sense filter. In the case of anomalous noises action in the observation channel with memory, we have proved insensitivity of the filter to inaccurate knowledge of the matrix of anomalous noise intensity and its equivalence to a truncated filter constructed only over non-anomalous components of an observation vector.
Publicat: 2017
Matèries:
Accés en línia:http://dx.doi.org/10.1088/1742-6596/803/1/012129
http://earchive.tpu.ru/handle/11683/38182
Format: Electrònic Capítol de llibre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654413
Descripció
Sumari:Title screen
We consider stochastic systems with continuous time over observations with memory in the presence of an anomalous noise. The paper is devoted to analysis of some properties of an optimal unbiased in mean-square sense filter. In the case of anomalous noises action in the observation channel with memory, we have proved insensitivity of the filter to inaccurate knowledge of the matrix of anomalous noise intensity and its equivalence to a truncated filter constructed only over non-anomalous components of an observation vector.
DOI:10.1088/1742-6596/803/1/012129