2014
DOI: 10.1088/1674-4527/14/3/007
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Comparison of three filters in asteroid-based autonomous navigation

Abstract: At present, optical autonomous navigation has become a key technology in deep space exploration programs. Recent studies focus on the problem of orbit determination using autonomous navigation, and the choice of filter is one of the main issues. To prepare for a possible exploration mission to Mars, the primary emphasis of this paper is to evaluate the capability of three filters, the extended Kalman filter (EKF), unscented Kalman filter (UKF) and weighted least-squares (WLS) algorithm, which have different in… Show more

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Cited by 3 publications
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“…27 shows the distribution of true flyby distances for each asteroid mass, when noise terms are added to the initial condition with standard deviations on position and velocity components of r ¼ 5 km and r ¼ 10 cm/s respectively as order-of-magnitude estimates of the possible uncertainty of the state elements relative to the asteroid from the guidance, navigation and control subsystem. These estimates were obtained by looking at past missions and concepts for deep space navigation relative to asteroids (Ozaki et al, 2016) (Accomazzo et al, 2010) (Hashimoto et al, 2010) (Cui and Zhu, 2014). Fig.…”
Section: Sensitivity To Initial Conditions and Asteroid Massmentioning
confidence: 99%
“…27 shows the distribution of true flyby distances for each asteroid mass, when noise terms are added to the initial condition with standard deviations on position and velocity components of r ¼ 5 km and r ¼ 10 cm/s respectively as order-of-magnitude estimates of the possible uncertainty of the state elements relative to the asteroid from the guidance, navigation and control subsystem. These estimates were obtained by looking at past missions and concepts for deep space navigation relative to asteroids (Ozaki et al, 2016) (Accomazzo et al, 2010) (Hashimoto et al, 2010) (Cui and Zhu, 2014). Fig.…”
Section: Sensitivity To Initial Conditions and Asteroid Massmentioning
confidence: 99%