Towards automated satellite conjunction management with Bayesian deep learning

After decades of space travel, low Earth orbit is a junkyard of discarded rocket bodies, dead satellites, and millions of pieces of debris from collisions and explosions.Objects in high enough altitudes do not re-enter and burn up in the atmosphere, but stay in orbit around Earth for a long time. With a speed of 28,000 km/h, collisions in these orbits can generate fragments and potentially trigger a cascade of more collisions known as the Kessler syndrome. This could pose a planetary challenge, because the phenomenon could escalate to the point of hindering future space operations and damaging satellite infrastructure critical for space and Earth science applications. As commercial entities place mega-constellations of satellites in orbit, the burden on operators conducting collision avoidance manoeuvres will increase.For this reason, development of automated tools that predict potential collision events (conjunctions) is critical. We introduce a Bayesian deep learning approach to this problem, and develop recurrent neural network architectures (LSTMs) that work with time series of conjunction data messages (CDMs), a standard data format used by the space community. We show that our method can be used to model all CDM features simultaneously, including the time of arrival of future CDMs, providing predictions of conjunction event evolution with associated uncertainties

Space assets and technology for bushfire management

The financial, emotional, and ecological impacts of bushfires can be devastating. This report was prepared by the participants of the Southern Hemisphere Space Studies Program 2021 in response to the topic: “How space assets and technologies can be applied to better predict and mitigate bushfires and their impacts.” To effectively reach the diverse set of stakeholders impacted by bushfires, Communication was identified as a key enabler central to any examination of the topic. The three pillars “predict”, “mitigate” and “communicate” were identified to frame the task at hand. Combining the diverse skills and experience of the class participants with the interdisciplinary knowledge gained from the seminars, distinguished lectures, and workshops during the SHSSP21 program, conducted a literature review With specific reference to the 2019-20 Australian fire season, we looked at the current state of the art, key challenges, and how bushfires can be better predicted and mitigated in the future. Comparing this to the future desired state, we identified gaps for each of the three domains, and worked across teams to reach consensus on a list of recommendations. Several of these recommendations were derived independently by two or more of the three groups, highlighting the importance of a holistic and collaborative approach. The report details a number of recommendations arising from this Where applicable, we also aligned our discussion with the experience and lessons from other countries and agencies to consider,learn from and respond to the international context, as others develop systems using space technology to tackle similar wildfire issues

Spacecraft collision risk assessment with probabilistic programming

Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat to operational spacecraft. Furthermore, the predicted growth of the space sector and the planned launch of megaconstellations will add even more complexity, therefore causing the collision risk and the burden on space operators to increase. Managing this complex framework with internationally agreed methods is pivotal and urgent. In this context, we build a novel physics based probabilistic generative model for synthetically generating conjunction data messages, calibrated using real data. By conditioning on observations, we use the model to obtain posterior distributions via Bayesian inference. We show that the probabilistic programming approach to conjunction assessment can help in making predictions and in finding the parameters that explain the observed data in conjunction data messages, thus shedding more light on key variables and orbital characteristics that more likely lead to conjunction events. Moreover, our technique enables the generation of physically accurate synthetic datasets of collisions, answering a fundamental need of the space and machine learning communities working in this area

Towards Automated Satellite Conjunction Management with Bayesian Deep Learning

After decades of space travel, low Earth orbit is a junkyard of discarded rocket bodies,dead satellites, and millions of pieces of debris from collisions and explosions.Objects in high enough altitudes do not re-enter and burn up in the atmosphere, butstay in orbit around Earth for a long time. With a speed of 28,000 km/h, collisionsin these orbits can generate fragments and potentially trigger a cascade of morecollisions known as the Kessler syndrome. This could pose a planetary challenge,because the phenomenon could escalate to the point of hindering future spaceoperations and damaging satellite infrastructure critical for space and Earth scienceapplications. As commercial entities place mega-constellations of satellites in orbit,the burden on operators conducting collision avoidance manoeuvres will increase.For this reason, development of automated tools that predict potential collisionevents (conjunctions) is critical. We introduce a Bayesian deep learning approachto this problem, and develop recurrent neural network architectures (LSTMs) thatwork with time series of conjunction data messages (CDMs), a standard data formatused by the space community. We show that our method can be used to modelall CDM features simultaneously, including the time of arrival of future CDMs,providing predictions of conjunction event evolution with associated uncertainties

Spacecraft Collision Risk Assessment with Probabilistic Programming

Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat to operational spacecraft. Furthermore, the predicted growth of the space sector and the planned launch of megaconstellations will add even more complexity, therefore causing the collision risk and the burden on space operators to increase. Managing this complex framework with internationally agreed methods is pivotal and urgent. In this context, we build a novel physics-based probabilistic generative model for synthetically generating conjunction data messages, calibrated using real data. By conditioning on observations, we use the model to obtain posterior distributions via Bayesian inference. We show that the probabilistic programming approach to conjunction assessment can help in making predictions and in finding the parameters that explain the observed data in conjunction data messages, thus shedding more light on key variables and orbital characteristics that more likely lead to conjunction events. Moreover, our technique enables the generation of physically accurate synthetic datasets of collisions, answering a fundamental need of the space and machine learning communities working in this area. Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada