Orbital Prediction Error Propagation of Space Objects

Chen, Lei; Bai, Xian-Zong; Liang, Yangang; Li, Kebo

doi:10.1007/978-981-10-2963-9_2

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Its origin is located at the centroid of the space object. The radial axis always points from the Earth’s center along the radius vector toward the satellite as it moves through the orbit; the along-track axis (or transverse) points in the direction of (but not necessarily parallel to) the velocity vector and is perpendicular to the radius vector; the cross-track axis is normal to the plane identified by velocity and radius vectors [16].…”

Section: Methodsmentioning

confidence: 99%

Signal in Space Error and Ephemeris Validity Time Evaluation of Milena and Doresa Galileo Satellites

Robustelli

Benassai

Pugliano

2019

Sensors

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In August 2016, Milena (E14) and Doresa (E18) satellites started to broadcast ephemeris in navigation message for testing purposes. If these satellites could be used, an improvement in the position accuracy would be achieved. A small error in the ephemeris would impact the accuracy of positioning up to ±2.5 m, thus orbit error must be assessed. The ephemeris quality was evaluated by calculating the SISEorbit (in orbit Signal In Space Error) using six different ephemeris validity time thresholds (14,400 s, 10,800 s, 7200 s, 3600 s, 1800 s, and 900 s). Two different periods of 2018 were analyzed by using IGS products: DOYs 52–71 and DOYs 172–191. For the first period, two different types of ephemeris were used: those received in IGS YEL2 station and the BRDM ones. Milena (E14) and Doresa (E18) satellites show a higher SISEorbit than the others. If validity time is reduced, the SISEorbit RMS of Milena (E14) and Doresa (E18) greatly decreases differently from the other satellites, for which the improvement, although present, is small. Milena (E14) and Doresa (E18) reach a SISEorbit RMS of about 1 m (comparable to that of the other Galileo satellites reach with the nominal validity time) when validity time of 1800 s is used. Therefore, using this threshold, the two satellites could be used to improve single point positioning accuracy.

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Section: Methodsmentioning

confidence: 99%

Signal in Space Error and Ephemeris Validity Time Evaluation of Milena and Doresa Galileo Satellites

Robustelli

Benassai

Pugliano

2019

Sensors

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“…The S axis, which is named along-track, is defined inside the orbital plane and is perpendicular to the radial axis. Finally, the W axis, which is called the cross-track, is normal to the orbital plane and thus perpendicular to both the Q and S axes (Chen et al, 2017). The QSW frame is also referred to as the RSW frame.…”

Section: Qsw Reference Framementioning

confidence: 99%

Precise Relative Navigation in Medium Earth Orbits with Global Navigation Satellite Systems and Intersatellite Links for Black Hole Imaging

Salas,

Fernández,

van den IJssel

2024

Preprint

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The Event Horizon Telescope (EHT) is a ground-based array of Very Long Baseline Interferometry (VLBI) telescopes designed to image the event horizon of black holes. To overcome its limitations, this work explores a mission concept involving a two-satellite constellation of VLBI telescopes deployed in circular and polar Medium Earth Orbit (MEO) at more than 8000 km altitude.  The attainment of high-resolution black hole images requires extremely precise baseline determination at the few millimetre level. To address this challenge, each satellite within the constellation is equipped with two Global Navigation Satellite System (GNSS) receivers and an optical Intersatellite Link (ISL) for relative navigation. This work assesses the feasibility of achieving highly accurate relative positioning within the constellation, particularly considering the large intersatellite distances involved. The methodology employed in this simulation study encompasses several steps. Initially, the satellite orbits are estimated independently for each satellite using GNSS observations. Following this, the orbit of one of the satellites is held fixed as a reference, while the orbit of the other satellite is re-estimated by incorporating the ISL observations. To enhance the accuracy of the orbit estimation, integer GNSS ambiguity resolution is implemented in the precise orbit determination process. The simulated data incorporates an extensive set of realistic error sources, including thermal noise, instrumental delays, clock biases, errors in the GNSS ephemerides and clocks, uncertainties in the geopotential and solar radiation pressure models, and white noise in the ISL observations.The results highlight the importance of integer ambiguity resolution in meeting the stringent relative navigation requirements of the mission. The analysis also reveals that the ISL observations primarily improve the baseline estimation along the direction of the link itself. However, in the direction of the black hole, the impact of ISL observations is minimal, indicating that the ISL does not significantly contribute to meeting the specific relative navigation requirements. Furthermore, the study identifies that large intersatellite distances lead to degraded relative orbit accuracy due to fewer shared errors between the two satellites. The work will show the accuracy obtained with the simulations, the assumptions considered, and the next steps needed.

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“…Measurement errors are all errors associated with the difference between the real state of the satellite (such as position and velocity) and the measured state, including numerical truncation errors, as well as other navigation errors caused by clock accuracy or coordinate systems precision. In Figure 3 we can see a summary of the error sources in orbit prediction, as presented by [10] and [53].…”

Section: Orbit Predictionmentioning

confidence: 99%

Machine Learning in Orbit Estimation: a Survey

Caldas¹,

Soares²

2022

Preprint

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Since the late '50s, when the first artificial satellite was launched, the number of resident space objects (RSOs) has steadily increased. It is estimated that around 1 Million objects larger than 1 cm are currently orbiting the Earth, with only 30,000, larger than 10 cm, presently being tracked. To avert a chain reaction of collisions, termed Kessler Syndrome [1], it is indispensable to accurately track and predict space debris and satellites' orbit alike. Current physics-based methods have errors in the order of kilometres for 7 days predictions, which is insufficient when considering space debris that have mostly less than 1 meter. Typically, this failure is due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, as well as specific unknown characteristics such as mass or geometry of the RSO. Leveraging datadriven techniques, namely machine learning, the orbit prediction accuracy can be enhanced: by deriving unmeasured objects' characteristics, improving non-conservative forces' effects, and by the superior abstraction capacity that Deep Learning models have of modelling highly complex non-linear systems. In this survey, we provide an overview of the current work being done in this field.

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Orbital Prediction Error Propagation of Space Objects

Cited by 3 publications

References 12 publications

Signal in Space Error and Ephemeris Validity Time Evaluation of Milena and Doresa Galileo Satellites

Signal in Space Error and Ephemeris Validity Time Evaluation of Milena and Doresa Galileo Satellites

Precise Relative Navigation in Medium Earth Orbits with Global Navigation Satellite Systems and Intersatellite Links for Black Hole Imaging

Machine Learning in Orbit Estimation: a Survey

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