Propagation errors analysis of TLE data

Wang, Rui; Liu, J.; Zhang, Q.M.

doi:10.1016/j.asr.2008.11.017

Cited by 46 publications

(15 citation statements)

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“…The accuracy of the angular data is usually at the level of 2″–5″ [ 7 ], which is equivalent to 10–25 m at a distance of 1000 km. The accuracy of the TLE-computed positions is usually at hundreds of meters [ 10 , 26 ]. This raises the question on how to assign appropriate weights to the angular data and TLE-computed positions in the OD computation.…”

Section: Methodsmentioning

confidence: 99%

A Real-Time Orbit Determination Method for Smooth Transition from Optical Tracking to Laser Ranging of Debris

Sang

Zhang

2016

Sensors

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A critical requirement to achieve high efficiency of debris laser tracking is to have sufficiently accurate orbit predictions (OP) in both the pointing direction (better than 20 arc seconds) and distance from the tracking station to the debris objects, with the former more important than the latter because of the narrow laser beam. When the two line element (TLE) is used to provide the orbit predictions, the resultant pointing errors are usually on the order of tens to hundreds of arc seconds. In practice, therefore, angular observations of debris objects are first collected using an optical tracking sensor, and then used to guide the laser beam pointing to the objects. The manual guidance may cause interrupts to the laser tracking, and consequently loss of valuable laser tracking data. This paper presents a real-time orbit determination (OD) and prediction method to realize smooth and efficient debris laser tracking. The method uses TLE-computed positions and angles over a short-arc of less than 2 min as observations in an OD process where simplified force models are considered. After the OD convergence, the OP is performed from the last observation epoch to the end of the tracking pass. Simulation and real tracking data processing results show that the pointing prediction errors are usually less than 10″, and the distance errors less than 100 m, therefore, the prediction accuracy is sufficient for the blind laser tracking.

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Section: Methodsmentioning

confidence: 99%

A Real-Time Orbit Determination Method for Smooth Transition from Optical Tracking to Laser Ranging of Debris

Sang

Zhang

2016

Sensors

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“…If the RSO is visible to a station, the station will provide measurements including the azimuth α, the elevation η, and the range ρ. These measurements are generated by converting the state vector of the RSO in the Earth Centered Inertial (ECI) frame (Vallado, 1997) to the topocentric frame and then calculating the angle information. A series of consecutive measurements are organized as a track.…”

Section: Measurement Modelsmentioning

confidence: 99%

“…Notice that the relative prediction error ε(t i ; t i−1 ) is identical to the definition of the consistency error between two estimations in the work of Rivera and Bai (2016). These orbit prediction errors often represented in the RSW coordinate frame, where x-axis (radial) is the radial direction, the y-axis (along-track) is perpendicular to the x-axis in the orbital plane and points to the inertial velocity direction, and the z axis (cross-track) is along the angular momentum direction (Vallado, 1997). Next, we construct the dataset for the ML model.…”

Section: Ml-modificationmentioning

confidence: 99%

“…The support vector machine (SVM) regression method (Smola and Schölkopf, 2004), which is an advanced method proven to be robust to outliers in the dataset, is chosen in this paper, although the proposed methodology is expected to be general and applicable to other ML methods. Furthermore, in contrast with other researches that focus on TLE data (Legendre et al, 2006;Sang et al, 2014Sang et al, , 2013Wang et al, 2009), we use a simulation-based space catalog environment to validate the proposed orbit prediction method. This approach is important, because a known "truth" will underlie all simulations, and the solutions from the machine learning can be compared to the "truth" to make definitive statements on the performance.…”

Section: Introductionmentioning

confidence: 99%

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Improving orbit prediction accuracy through supervised machine learning

Peng

Bai

2018

Advances in Space Research

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Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: 1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; 2) the ML model can be used to improve predictions of the same RSO at future epochs; and 3) the ML model based on a RSO can be applied to other RSOs that share some common features.

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“…Pearlman, Degnan, & Bosworth (2002) 2 Hayden (1962); Gilbreath (1997); Schumacher et al (2001) 3 Neal, Coffey, & Knowles (1997); Coffey et al (1998); Boers et al (2000) 4 Boyce (2004); Chan & Navarro (2001); Flohrer, Krag, & Klinkrad (2009);Kelso (2007);Muldoon & Elkaim (2008);Snow & Kaya (1999);Wang, Liu, & Zhang (2009) …”

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confidence: 99%

Improved orbit predictions using two-line elements

Levit

Marshall

2011

Advances in Space Research

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The density of orbital space debris constitutes an increasing environmental challenge. There are three ways to alleviate the problem: debris mitigation, debris removal and collision avoidance. This paper addresses collision avoidance, by describing a method that contributes to achieving a requisite increase in orbit prediction accuracy. Batch least-squares differential correction is applied to the publicly available two-line element (TLE) catalog of space objects. Using a high-precision numerical propagator, we fit an orbit to state vectors derived from successive TLEs. We then propagate the fitted orbit further forward in time. These predictions are compared to precision ephemeris data derived from the International Laser Ranging Service (ILRS) for several satellites, including objects in the congested sun-synchronous orbital region. The method leads to a predicted range error that increases at a typical rate of 100 meters per day, approximately a 10-fold improvement over TLE's propagated with their associated analytic propagator (SGP4). Corresponding improvements for debris trajectories could potentially provide initial conjunction analysis sufficiently accurate for an operationally viable collision avoidance system. We discuss additional optimization and the computational requirements for applying all-on-all conjunction analysis to the whole TLE catalog, present and near future. Finally, we outline a scheme for debris-debris collision avoidance that may become practicable given these developments.Comment: Submitted to Advances in Space Research. 13 pages, 4 figure

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Propagation errors analysis of TLE data

Cited by 46 publications

References 1 publication

A Real-Time Orbit Determination Method for Smooth Transition from Optical Tracking to Laser Ranging of Debris

A Real-Time Orbit Determination Method for Smooth Transition from Optical Tracking to Laser Ranging of Debris

Improving orbit prediction accuracy through supervised machine learning

Improved orbit predictions using two-line elements

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