An analysis of close approaches and probability of collisions between LEO resident space objects and mega constellations

Zhang, Yan; Li, Bin; Liu, Hongkang; Sang, Jizhang

doi:10.1080/10095020.2022.2031313

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…China also proposed a Hongyan Constellation System with 60 nodes and global data processing center. It intends to provide users with global realtime data communications and integrated information services under complex weather and terrain conditions [30].…”

Section: Leo Satellites For Remote Sensing and On-orbit Computingmentioning

confidence: 99%

LEO Satellite Constellation for Global-Scale Remote Sensing With On-Orbit Cloud AI Computing

Li,

Wang,

Hwang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper proposes a new satellite-based framework for global-scale remote sensing that is integrated with on-orbit cloud computing and AI (artificial intelligence) services. These spaced-based services cover the entire earth surfaces using massive LEO satellite constellation. Global-scale sensing of earth resources must be supported by massive number of LEO satellites equipped with cloud/AI computing services in real time. New satellite computer architectural features are presented along with some satellite constellation deployment topologies.We design satellite-based computers to support on-orbit remote sensing and AI scene analysis. This demands real-time performance without transmitting the sensed data back to earth for delayed processing. Notable space data services include onorbit data sensing of large areas, machine learning from earth resources data, earth scene/event analysis, geomorphology observation, smart city management, disaster relief, global healthcare IoT, and environmental ecology protection, etc. We attempt to achieve high-efficiency earth resources utilization along with green energy, low cost, and robustness in real-life services.

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Section: Leo Satellites For Remote Sensing and On-orbit Computingmentioning

confidence: 99%

LEO Satellite Constellation for Global-Scale Remote Sensing With On-Orbit Cloud AI Computing

Li,

Wang,

Hwang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…With the employment of more accurate sensors and the growing population of RSOs (Zhang et al 2022), the potential number of newly discovered objects is likely to increase by an order of magnitude within the next decade, thereby placing an ever-increasing burden on current operational systems. The above limitations have made existing techniques ill-suited for future needs.…”

Section: Introductionmentioning

confidence: 99%

A Data-driven Method for Realistic Covariance Prediction of Space Object with Sparse Tracking Data

Liu

Zhou

et al. 2023

Res. Astron. Astrophys.

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Covariance of the orbital state of a resident space object (RSO) is a necessary requirement for various space situational awareness tasks, like the space collision warning. It describes an accuracy envelope of the RSO’s location. However, in current space surveillance, the tracking data of an individual RSO is often found insufficiently accurate and sparsely distributed, making the predicted covariance (PC) derived from the tracking data and classical orbit dynamic system usually unrealistic in describing the error characterization of orbit predictions. Given the fact that the tracking data of a RSO from a single station or a fixed network shares a similar temporal and spatial distribution, the evolution of PC could share a hidden relationship with that data distribution. This study proposes a novel method to generate accurate PC by combining the classical covariance propagation method and the data-driven approach. Two popular machine learning algorithms are applied to model the inconsistency between the orbit prediction error and the PC from historical observations, and then this inconsistency model is used to the future PC. Experimental results with the Swarm constellation satellites demonstrate that the trained Random Forest models can capture more than 95% of the underlying inconsistency in a tracking scenario of sparse observations. More importantly, the trained models show great generalization capability in correcting the PC of future epochs and other RSOs with similar orbit characteristics and observation conditions. Besides, a deep analysis of generalization performance is carried out to describe the temporal and spatial similarities of two data sets, in which the Jaccard similarity is used. It demonstrates that the higher the Jaccard similarity is, the better the generalization performance will be, which may be used as a guide to whether to apply the trained models of a satellite to other satellites. Further, the generalization performance is also evaluated by the classical Cramer von Misses test, which also shows that trained models have encouraging generalization performance.

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“…Peng and Bai proposed applying machine learning method to two-line element (TLE) data to improve the accuracy of orbit prediction [11]. In terms of applications, Zhang et al analysed close approaches and the probability of collisions between the LEO resident space objects (RSOs) and mega-constellations [12]. Using space-based tracking data of RSOs, Hussain et al performed collision avoidance experiments on distributed satellite systems [13].…”

Section: Introductionmentioning

confidence: 99%

New Space Object Cataloguing through Very-Short-Arc Data Mining

Liu,

Li,

Sang

et al. 2023

Remote Sensing

Self Cite

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The space surveillance network collects significant quantities of space object monitoring data on a daily basis, which varies in duration and contain observation errors. Cataloguing space objects based on these data may result in a large number of very short arcs (VSAs) being wasted due to cataloguing flaws, poor data quality, data precessing, and so on. To address this problem, an effective data mining method based on tracklet-to-object matching is proposed to improve the data utilization in new object cataloguing. The method can enhance orbital constraints based on useful track information in mined tracklets, improve the accuracy of catalogued orbits, and achieve the transformation of omitted observations into “treasures”. The performance of VSAs is evaluated in tracklet-to-object matching, which is less sensitive to tracklet duration and separation time than initial orbit determination (IOD) and track association. Further, the data mining method is applied to new space object cataloguing based on radar tracklets and achieved significant improvements. The 5-day data utilization increased by 9.5%, and the orbit determination and prediction accuracy increased by 11.1% and 23.6%, respectively, validating the effectiveness of our method in improving the accuracy of space object orbit cataloguing. The method shows promising potential for the space object cataloguing and relevant applications.

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An analysis of close approaches and probability of collisions between LEO resident space objects and mega constellations

Cited by 10 publications

References 23 publications

LEO Satellite Constellation for Global-Scale Remote Sensing With On-Orbit Cloud AI Computing

LEO Satellite Constellation for Global-Scale Remote Sensing With On-Orbit Cloud AI Computing

A Data-driven Method for Realistic Covariance Prediction of Space Object with Sparse Tracking Data

New Space Object Cataloguing through Very-Short-Arc Data Mining

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