Comparison of EKF and UKF for Spacecraft Localization via Angle Measurements

Giannitrapani, Antonio; Ceccarelli, Nicola; Scortecci, Fabrizio; Garulli, Andrea

doi:10.1109/taes.2011.5705660

Cited by 97 publications

(60 citation statements)

References 18 publications

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“…In [4], the author also faces the problem of robot localization and he concludes, on the basis of simulated data, that the UKF is superior when the same kind of sensors here considered are used, while the two filters perform similarly when RFID landmarks are used. In [5] the problem of a spacecraft localization from real angular measurements has been faced using both the EKF and the UKF, and there the UKF has proven to be superior, within the same order of magnitude, in terms of both localization accuracy and consistence of the estimates. Furthermore, in [6] the two filters have been shown to perform equivalently in human head/hand motion tracking for virtual reality applications, while [7] suggests that the UKF performs better in tracking a ballistic target.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot localization via EKF and UKF: A comparison based on real data

D’Alfonso

Lúcia

Muraca

et al. 2015

Robotics and Autonomous Systems

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Section: Introductionmentioning

confidence: 99%

Mobile robot localization via EKF and UKF: A comparison based on real data

D’Alfonso

Lúcia

Muraca

et al. 2015

Robotics and Autonomous Systems

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“…Ceccarelli et al (2007) applied the Unscented Kalman Filter (UKF) to the problem of spacecraft localisation using only angle measurements. Giannitrapani et al (2011) presented a comparison of Extended Kalman Filter (EKF) and UKF for spacecraft localisation via angle measurements.…”

Section: J O I N T P O S I T I O N L O C a L I S At I O N O F S Pa C mentioning

confidence: 99%

Joint Position Localisation of Spacecraft and Debris for Autonomous Navigation Applications using Angle Measurements only

Vepa

2017

J. Navigation

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In this paper, we consider the joint estimation of the position of a spacecraft and debris in Earth orbit to achieve spacecraft localisation based on angular measurements and precise measurements of the debris relative to the spacecraft. The dynamic model of the spacecraft caters for several perturbing effects, such as Earth and Moon gravitational field asymmetry and the Earth's oblateness effect. The Moon's position is assumed to be accurately known for the purposes of simulation from published Jet Propulsion Laboratory (JPL) ephemerides. The measurement process is based on the elevation and azimuth of the Moon and the Sun with respect to the spacecraft reference system. Range measurements are not assumed to be available. Position and velocity of the spacecraft are estimated by using the Unscented Kalman Filter (UKF). The performance of the filters are evaluated on an example of an Earth-orbiting satellite at an altitude over 1200 km with measurements of the directions of the Moon and the Sun only. It is shown that the estimates of position and velocity components track the corresponding simulated position and velocity components.

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“…UKF is similar to the EKF but the linearization process is not applied in UKF. Because EKF is one of the most widely used filters for nonlinear systems, specifically because of its speed, this filter is employed in the current research (Giannitrapani et al, 2011). In fact, this filter converts the nonlinear system into a state-dependent time varying linear system; however, this transformation leads to some modelling errors.…”

Section: No 4 P O S I T I O N I N G a N D T R A J E C T O Ry P R E Dmentioning

confidence: 99%

Robust Positioning, Preliminary Orbit Determination, and Trajectory Prediction of Space Debris using In-Space Iterative-Bearing-Only Observations

2017

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This paper is concerned with the preliminary localisation, orbit determination and model-based path forecasting of space debris based on a robust procedure. In this work, an in-orbit observer utilises only relative bearing observations iteratively. To this end, the problem is first formulated in order to calculate the distance vector between the space debris and any orbiting observer. Afterwards, the obtained position vector is corrected through an Extended Kalman Filter (EKF) for shrinking the sensor and process errors and increasing robustness of the computations in the presence of uncertainties. After preliminary positioning, the related classical orbital elements are acquired via the predicted position and velocity vectors using a hybrid technique. Extensive simulations demonstrate the efficacy and robustness of the aforementioned method, and in particular it is verified that the proposed scheme is capable of producing a suitable solution for preliminary localisation and orbit determination of space debris based on the presented space-based observation, which is practical in phasing and chasing manoeuvres of any grabber space robot. K E Y

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Comparison of EKF and UKF for Spacecraft Localization via Angle Measurements

Cited by 97 publications

References 18 publications

Mobile robot localization via EKF and UKF: A comparison based on real data

Mobile robot localization via EKF and UKF: A comparison based on real data

Joint Position Localisation of Spacecraft and Debris for Autonomous Navigation Applications using Angle Measurements only

Robust Positioning, Preliminary Orbit Determination, and Trajectory Prediction of Space Debris using In-Space Iterative-Bearing-Only Observations

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