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Two-Stage Parametric Identification Procedure for a Satellite Motion Model Based on Adaptive Unscented Kalman Filters

Authors: Chernikova, O.S.; Grechkoseev, A.K.; Danchenko, I.G.;

Two-Stage Parametric Identification Procedure for a Satellite Motion Model Based on Adaptive Unscented Kalman Filters

Abstract

O.S. Chernikova1, A.K. Grechkoseev2, I.G. Danchenko1 1Novosibirsk State Technical University, Novosibirsk, Russian Federation 2JSC Academician M.F. Reshetnev “Information Satellite System” , Zheleznogorsk, Russian Federation E-mail: chernikova@corp.nstu.ru, gak@iss-reshetnev.ru, danchenko.ivan@inbox.ru Оксана Сергеевна Черникова, кандидат технических наук, доцент, старший научный сотрудник, кафедра теоретической и прикладной информатики, Новосибирский государственный технический университет (г. Новосибирск, Российская Федерация), chernikova@corp.nstu.ru. Александр Кузьмич Гречкосеев, доктор технических наук, главный специалист АО ≪Информационные спутниковые системы≫ имени академика М.Ф. Решетнева (г. Железногорск, Российская Федерация), gak@iss-reshetnev.ru. Иван Геннадьевич Данченко, студент факультета прикладной математики и ин- форматики, Новосибирский государственный технический университет (г. Новоси- бирск, Российская Федерация), danchenko.ivan@inbox.ru. The paper presents a new two-stage parametric identification procedure for constructing a navigation satellite motion model. At the first stage of the procedure, the parameters of the radiation pressure model are estimated using the maximum likelihood method and the multiple adaptive unscented Kalman filter. At the second stage, the parameters of the unaccounted perturbations model are estimated based on the results of residual differences measurements. The obtained results lead to significant improvement of prediction quality of the satellite trajectory. В работе представлена новая двухэтапная процедура параметрической идентификации модели движения центра масс космического аппарата. На первом этапе процедуры с помощью метода максимального правдоподобия оцениваются параметры модели радиационного давления, при этом построение критерия идентификации осуществляется на основе нескольких адаптивных модификаций непрерывно-дискретного сигма-точечного фильтра Калмана. На втором этапе процедуры по результатам измерений остаточных разностей строится регрессионная модель неучтенных возмущений. Полученные численные результаты приводят к значительному улучшению точности прогнозирования траектории движения космического аппарата.

Keywords

модель движения космического аппарата, нелинейная стохастическая непрерывно-дискретная система, nonlinear stochastic continuous-discrete system, адаптивный сигма-точечный фильтр, parametric identification, satellite orbital motion model, ML method, multiple adaptive unscented Kalman filter, УДК 521.3, параметрическая идентификация, радиационное давление

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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