Satellite Orbit Prediction Using Big Data and Soft Computing Techniques to Avoid Space Collisions

Puente, Cristina; Nuño, María Ana Sáenz; Monte, Augusto Villa; Olivas, José Á.

doi:10.3390/math9172040

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…The goal is to improve the accuracy of monitoring data and the reliability of satellite orbits. Inaccurate monitoring and prediction of space object orbits can have severe consequences, such as satellite collisions that may destroy satellites and produce additional space debris that could result in further collisions [2]. For example, the collision between Iridium 33 and deactivated Kosmos 2251 on 10 February 2009 resulted in approximately 2000 additional pieces of space debris [3].…”

Section: Introductionmentioning

confidence: 99%

Research on Enhanced Orbit Prediction Techniques Utilizing Multiple Sets of Two-Line Element

Chen

Lin

2023

Aerospace

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Acquiring accurate space object orbits is crucial for many applications such as satellite tracking, space debris detection, and collision avoidance. The widely used two-line element (TLE) method estimates the position and velocity of objects in space, but its accuracy can be limited by various factors. A combination of multiple TLEs and advanced modeling techniques such as batch least squares differential correction and high-precision numerical propagators can significantly improve TLE accuracy and reliability, ensuring better space object surveillance. Previous studies analyzed additional factors that may influence TLE accuracy and evaluated the accuracy of Starlink TLE using precise ephemeris data from SpaceX. The results indicate that utilizing multiple TLEs for precise orbit determination can significantly enhance the performance of orbit prediction methods, particularly when compared to SGP4. By leveraging 10-day Starlink TLEs, the accuracy of 5-day predictions can be improved by approximately twofold. Additionally, producing two pseudo-observations within an orbital period near the TLE epoch yields the greatest effect on prediction accuracy, with this distribution of pseudo-observations increasing accuracy by approximately 10% compared to a uniform distribution. Further research can explore more data fusion and machine learning approaches to optimize operations in space.

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Section: Introductionmentioning

confidence: 99%

Research on Enhanced Orbit Prediction Techniques Utilizing Multiple Sets of Two-Line Element

Chen

Lin

2023

Aerospace

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“…Advanced computer technology has combined satellite orbit theory with precise numerical integration, enabling the consideration of very complex mechanical models and achieving precise satellite OD [1]. Modern space tracking technologies, such as GNSS and SLR [2], provide widespread and dense satellite observation data with millimeter-level accuracy, realizing centimeter-level and normalized OD precision for many satellites used for Earth observations and navigation [3].…”

Section: Introductionmentioning

confidence: 99%

A Case Study on the Effect of Atmospheric Density Calibration on Orbit Predictions with Sparse Angular Data

Chen

Sang

et al. 2023

Remote Sensing

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Accurately modeling the density of atmospheric mass is critical for orbit determination and prediction of space objects. Existing atmospheric mass density models (ADMs) have an accuracy of about 15%. Developing high-precision ADMs is a long-term goal that requires a better understanding of atmospheric density characteristics, more accurate modeling methods, and improved spatiotemporal data. This study proposes a method for calibrating ADMs using sparse angular data of space objects in low-Earth orbit over a certain period of time. Applying the corrected ADM not only improves the accuracy of orbit determination, but also enhances the accuracy of orbit prediction beyond the correction period. The study compares the impact of two calibration methods: atmospheric mass density model coefficient (ADMC) calibration and high precision satellite drag model (HASDM) calibration on the accuracy of orbit prediction of space objects. One month of ground-based telescope array angular data is used to validate the results. Space objects are classified as calibration objects, participating in ADM calibration, and verification objects, inside and outside the calibration orbit region, respectively. The results show that applying the calibrated ADM can significantly increase the accuracy of orbit prediction. For objects within the calibration orbit region, the calibration object’s orbit prediction error was reduced by about 55%, while that of verification objects was reduced by about 45%. The reduction in orbit prediction error outside this region was about 30%. This proposed method contributes significantly to the development of more reliable ADMs for orbit prediction of space objects with sparse angular data and can provide significant academic value in the field of space situational awareness.

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“…Plus, there are few evaluation studies on the reform effect. Moreover, since the world has entered the era of big data, big data have become a major focus of academia, industry, and government agencies [41][42][43]. Big data technology is gradually promoting the reform and innovation of talent training mode in universities.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Linguistic PROMETHEE I and II Methods for Evaluation of the Reform Scheme of Postgraduate Innovation and Entrepreneurship Education Talent Training Mode under the Big Data Environment

2022

Mathematical Problems in Engineering

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Talent training quality is an important field within higher education research. Innovating the talent training mode and deepening educational reform programs are both of great significance for enhancing the quality of postgraduate innovation and entrepreneurship education in universities. In this study, Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II methods are extended with the probability linguistic term set (PLTS) to accurately express and quantitatively evaluate the reform scheme of postgraduate innovation and entrepreneurship education talent training mode under the big data environment. First, probabilistic linguistic PROMETHEE I and II methods are presented for quantitatively evaluating the reform scheme of postgraduate innovation and entrepreneurship education talent training, which have the advantages of good effectiveness and feasibility. Second, the PLTS is imported into the evaluation methods and applied to accurately depict qualitative information about the index data of the reform scheme effect by the degree of probability. Third, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with PLTS is proposed to perform a comparative study and conduct visual analysis to verify the effectiveness of the extended probabilistic linguistic PROMETHEE I and II methods. Fourth, an empirical example illustrates the specific evaluation process, verifies the feasibility of the extended methods, and explains the effectiveness of the results. The research findings indicate that the proposed method to reform scheme evaluation can lead to better decision quality, especially in a complex fuzzy and uncertain decision-making environment.

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Satellite Orbit Prediction Using Big Data and Soft Computing Techniques to Avoid Space Collisions

Cited by 8 publications

References 6 publications

Research on Enhanced Orbit Prediction Techniques Utilizing Multiple Sets of Two-Line Element

Research on Enhanced Orbit Prediction Techniques Utilizing Multiple Sets of Two-Line Element

A Case Study on the Effect of Atmospheric Density Calibration on Orbit Predictions with Sparse Angular Data

Probabilistic Linguistic PROMETHEE I and II Methods for Evaluation of the Reform Scheme of Postgraduate Innovation and Entrepreneurship Education Talent Training Mode under the Big Data Environment

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