Onboard and real-time artificial satellite orbit determination using GPS


Autoria(s): Chiaradia, Ana Paula Marins; Kuga, Hélio Koiti; Prado, Antonio Fernando Bertachini de Almeida
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

27/05/2014

27/05/2014

05/04/2013

Resumo

An algorithm for real-time and onboard orbit determination applying the Extended Kalman Filter (EKF) method is developed. Aiming at a very simple and still fairly accurate orbit determination, an analysis is performed to ascertain an adequacy of modeling complexity versus accuracy. The minimum set of to-be-estimated states to reach the level of accuracy of tens of meters is found to have at least the position, velocity, and user clock offset components. The dynamical model is assessed through several tests, covering force model, numerical integration scheme and step size, and simplified variational equations. The measurement model includes only relevant effects to the order of meters. The EKF method is chosen to be the simplest real-time estimation algorithm with adequate tuning of its parameters. In the developed procedure, the obtained position and velocity errors along a day vary from 15 to 20 m and from 0.014 to 0.018 m/s, respectively, with standard deviation from 6 to 10 m and from 0.006 to 0.008 m/s, respectively, with the SA either on or off. The results, as well as analysis of the final adopted models used, are presented in this work. © 2013 Ana Paula Marins Chiaradia et al.

Identificador

http://dx.doi.org/10.1155/2013/530516

Mathematical Problems in Engineering, v. 2013.

1024-123X

1563-5147

http://hdl.handle.net/11449/75073

10.1155/2013/530516

WOS:000315888400001

2-s2.0-84875686038

2-s2.0-84875686038.pdf

Idioma(s)

eng

Relação

Mathematical Problems in Engineering

Direitos

openAccess

Palavras-Chave #Measurement model #Modeling complexity #Numerical integration scheme #Orbit determination #Real-time estimation #Satellite orbit determination #Standard deviation #Variational equations #Algorithms #Extended Kalman filters
Tipo

info:eu-repo/semantics/article