Optical Tracking Data Validation and Orbit Estimation for Sparse Observations of Satellites by the OWL-Net

Choi, Jin; Jo, Jung Hyun; Yim, Hong-Suh; Choi, Eun-Jung; Cho, Sungki; Park, Jang-Hyun

doi:10.3390/s18061868

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…Table 1 presents the standard deviation (1σ) values of the measurement residuals before and after the correction. After correction, the RA 1σ was reduced from 8-16 to approximately 5-6 arcsec and the DEC 1σ from 4-10 to approximately 2-7 arcsec, which corresponds to the expected quality level of the OWL-Net observation data (Choi et al 2018). Fig.…”

Section: Verification Of Modelsmentioning

confidence: 58%

“…There is room for improvement in the accuracy of POD by developing a more precise model later, but in this study, we compared the results by setting the same model for LS and UT batch filters. Note that the atmospheric drag coefficient was set as 1.9 (Choi et al 2018), and the solar radiation pressure coefficient was set as 0.7 (Root 2012) for the POD.…”

Section: Case Position Error (Km)mentioning

confidence: 99%

“…In the case of the estimation algorithms, two types of batch filters were used-the least-squares (LS) batch filter and the unscented transform (UT) batch filter. Previously conducted studies on OWL-Net (Lee et al 2017;Choi et al 2018) used LS batch filter, including a linearization process, but it may cause a large error or divergence when the initial condition is inaccurate (Park et al 2010). The UT batch filter, however, is a nonlinear filter that can overcome the disadvantages of linearization.…”

Section: Introductionmentioning

confidence: 99%

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Precise Orbit Determination Based on the Unscented Transform for Optical Observations

Hwang¹,

Lee²,

Park³

2019

Journal of Astronomy and Space Sciences

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In this study, the precise orbit determination (POD) software is developed for optical observation. To improve the performance of the estimation algorithm, a nonlinear batch filter, based on the unscented transform (UT) that overcomes the disadvantages of the least-squares (LS) batch filter, is utilized. The LS and UT batch filter algorithms are verified through numerical simulation analysis using artificial optical measurements. We use the real optical observation data of a low Earth orbit (LEO) satellite, Cryosat-2, observed from optical wide-field patrol network (OWL-Net), to verify the performance of the POD software developed. The effects of light travel time, annual aberration, and diurnal aberration are considered as error models to correct OWL-Net data. As a result of POD, measurement residual and estimated state vector of the LS batch filter converge to the local minimum when the initial orbit error is large or the initial covariance matrix is smaller than the initial error level. However, UT batch filter converges to the global minimum, irrespective of the initial orbit error and the initial covariance matrix.

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Section: Verification Of Modelsmentioning

confidence: 58%

Section: Case Position Error (Km)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Precise Orbit Determination Based on the Unscented Transform for Optical Observations

Hwang¹,

Lee²,

Park³

2019

Journal of Astronomy and Space Sciences

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“…It should not become an issue because NORAD is released the TLE data at regular periodic intervals [26]. The TLE data is known as the most comprehensive space object cataloging system in which the information is updated every 1-2 days for the expected target, and for the critical target, it will be updated 2-3 times every day [23][27] [28]. However, this issue still needs to be considered because we do not want to rely too heavily on TLE data to make orbital propagation.…”

Section: Resultsmentioning

confidence: 99%

Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network

Salleh¹,

Yuhaniz²,

Azmi³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Orbital propagation models are used to predict the position and velocity of natural and artificial objects orbiting the Earth. It is crucial to get accurate predictions to ensure proper satellite operational planning and early detection of possible disasters. It became critical as the number of space objects grew due to many countries scrambling to explore space for various purposes such as communications, remote sensing, scientific mission, and many more. Physical-based and mathematical expression approaches provide orbital propagation with high accuracy. However, these approaches require substantial expenditure to provide suitable facilities and are complicated for those with no expertise in this field. The orbital propagation model is developed using regression techniques and artificial neural networks in this study. The aim is to have a reliable and precise orbital propagation model with minimal computational and cost savings. The past orbital data is used instead of complicated numerical equations and expensive tools. As a result, the trained orbital propagation model with accuracy up to 99.49% with a distance error of 18.73km per minute is achievable. The trained model can be improved further by modifying the network model and various input data. This model is also expected to provide vital information for organizations and anyone interested. Finally, this research can help organizations with insufficient resources to have their orbit propagation model without special tools or rely on other countries with satellite data at a lower cost.

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“…Practically, the optical sensors are strongly affected by their observation environments which are subject to weather, schedule and visibility constraints. The latter is limited by terminator conditions which can restrict tracking sessions to periods before sun rise and after sun set [12,13]. Since laser space debris tracking sensors usually operate initially with an optical sensor to support acquisition [11], providing OP accuracy of 10-20 arc seconds (about 50-100 m at 1000 km altitude) allows for space debris laser tracking without optical sensors for initial acquisition (blind laser ranging).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Space Debris Orbit Prediction Using Angle and Laser Ranging Data from Two Tracking Sites under Limited Observation Environment

Kim

Lim

Bennett³

et al. 2020

Sensors

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The global electro-optical (EO) and laser tracking sensor network was considered to investigate improvements to orbit prediction (OP) accuracy of space debris by combining angle and laser ranging data. However, it is worth noting that weather, schedule and visibility constraints can frequently limit the operations of such sensors, which may not result in sufficient tracking data for accurate OP. In this study, several 1-day OP results for low Earth orbit (LEO) space debris targets were demonstrated under a limited observation environment to verify the OP accuracy through the combination of angle and laser ranging data from two sites. For orbit determination (OD) processes, it was considered to analyze the OP accuracy by one site providing both 2–day arc angle data and 1-day arc laser ranging data, while the other was limited to 1-day arc angle data. In addition, the initial ballistic coefficient ( B C ) application method was proposed and implemented for the improvement of OD/OP accuracy, which introduces the modified correction factor depending on the drag coefficient. In the cases of combining the angle and laser ranging data, the OP results show the 3D position difference values are below 100 m root mean square (RMS) with small position uncertainty. This value satisfies the target OP accuracy for conjunction assessments and blind laser ranging (about 50–100 m at 1000 km altitude). The initial B C application method also shows better OP accuracy than the method without the correction factor.

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Optical Tracking Data Validation and Orbit Estimation for Sparse Observations of Satellites by the OWL-Net

Cited by 11 publications

References 24 publications

Precise Orbit Determination Based on the Unscented Transform for Optical Observations

Precise Orbit Determination Based on the Unscented Transform for Optical Observations

Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network

Analysis of Space Debris Orbit Prediction Using Angle and Laser Ranging Data from Two Tracking Sites under Limited Observation Environment

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