Innovative observing strategy and orbit determination for Low Earth Orbit space debris

Milani, Andrea; Farnocchia, Davide; Dimare, Linda; Rossi, Alessandro; Bernardi, Fabrizio

doi:10.1016/j.pss.2011.11.012

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…Besides, optimization of the fine-scale observation strategy would require a lot of human interventions, and optimization of the large-scale observation strategy would have a complexity that is proportional to the number of telescopes and celestial objects, which would excede the capacities of any ordinary planning algorithms. In recent years, machine-learning-based strategy optimization algorithms have been widely discussed (Milani et al 2012;Ferreira et al 2016;Hinze et al 2016;Frueh et al 2018;Cai et al 2020). To reduce computation cost and complexity of these algorithms, there are many assumptions that would deviate from real observation conditions.…”

Section: Introductionmentioning

confidence: 99%

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

Jia

Jiang

et al. 2023

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Time-domain astronomy is an active research area now, which requires frequent observations of the whole sky to capture celestial objects with temporal variations. In the optical band, several telescopes in different locations could form a distributed telescope array to capture images of celestial objects continuously. However, there are millions of celestial objects to observe each night, and only limited telescopes could be used for observation. Besides, the observation capacity of these telescopes would be affected by different effects, such as the sky background or the seeing condition. It would be necessary to develop an algorithm to optimize the observation strategy of telescope arrays according to scientific requirements. In this paper, we propose a novel framework that includes a digital simulation environment and a deep reinforcement learning algorithm to optimize observation strategy of telescope arrays. Our framework could obtain effective observation strategies given predefined observation requirements and observation environment information. To test the performance of our algorithm, we simulate a scenario that uses distributed telescope arrays to observe space debris. Results show that our algorithm could obtain better results in both discovery and tracking of space debris. The framework proposed in this paper could be used as an effective strategy optimization framework for distributed telescope arrays, such as the Sitian project or the TIDO project.

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Section: Introductionmentioning

confidence: 99%

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

Jia

Jiang

et al. 2023

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“…The classical approach for orbit computation is the method of Gauss, which requires three observations of the same object, at different moments in time. Milani et al propose in [ 23 ] a new algorithm for orbit determination, based on the first integrals of the Kepler problem, which requires only two detections at different passes of the target object. The authors emphasize the need for accurate correlation between different observations of the same object, and propose sophisticated solutions for solving this problem in the case of high observation datasets.…”

Section: Introductionmentioning

confidence: 99%

A Low Cost Automatic Detection and Ranging System for Space Surveillance in the Medium Earth Orbit Region and Beyond

Danescu

Ciurte

Turcu

2014

Sensors

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The space around the Earth is filled with man-made objects, which orbit the planet at altitudes ranging from hundreds to tens of thousands of kilometers. Keeping an eye on all objects in Earth's orbit, useful and not useful, operational or not, is known as Space Surveillance. Due to cost considerations, the space surveillance solutions beyond the Low Earth Orbit region are mainly based on optical instruments. This paper presents a solution for real-time automatic detection and ranging of space objects of altitudes ranging from below the Medium Earth Orbit up to 40,000 km, based on two low cost observation systems built using commercial cameras and marginally professional telescopes, placed 37 km apart, operating as a large baseline stereovision system. The telescopes are pointed towards any visible region of the sky, and the system is able to automatically calibrate the orientation parameters using automatic matching of reference stars from an online catalog, with a very high tolerance for the initial guess of the sky region and camera orientation. The difference between the left and right image of a synchronized stereo pair is used for automatic detection of the satellite pixels, using an original difference computation algorithm that is capable of high sensitivity and a low false positive rate. The use of stereovision provides a strong means of removing false positives, and avoids the need for prior knowledge of the orbits observed, the system being able to detect at the same time all types of objects that fall within the measurement range and are visible on the image.

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“…In this case, for successful IOD, two or more TSAs need to be linked or correlated to the same physical object. The linkage and correlation problem between TSA's for orbit determination has been well documented in previous literature, [7]- [13]. While some work has concentrated particularly on this problem relative to space debris, [3], [6], [9], [14].…”

Section: Other Orbit Determination Methods and Associated Topicsmentioning

confidence: 99%

“…In [13] an algorithm is presented for the correlation and orbit determination of LEO objects from ground-based radar and optical measurements. The algorithm presented needs only two observations from different orbital passes, while studies reported in [1], [2], [4], [5] necessitate three observations from different orbital passes.…”

Section: Other Orbit Determination Methods and Associated Topicsmentioning

confidence: 99%

“…This is the case if two attributable belong to different objects. The orbit determination process for this algorithm in [13] represents preliminary two-body orbits and may possibly account for the J2 perturbation experienced by LEO space objects. A perturbation is the force acting on a satellite that perturbs it away from the nominal, Keplerian, orbit.…”

Section: Other Orbit Determination Methods and Associated Topicsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling, Simulation, and Characterization of Space Debris in low-Earth Orbit

McCall¹

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Innovative observing strategy and orbit determination for Low Earth Orbit space debris

Cited by 13 publications

References 25 publications

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

A Low Cost Automatic Detection and Ranging System for Space Surveillance in the Medium Earth Orbit Region and Beyond

Modeling, Simulation, and Characterization of Space Debris in low-Earth Orbit

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