Probability of Collision of satellites and space debris for short-term
encounters: Rederivation and fast-to-compute upper and lower bounds
- URL: http://arxiv.org/abs/2311.08978v1
- Date: Wed, 15 Nov 2023 14:12:55 GMT
- Title: Probability of Collision of satellites and space debris for short-term
encounters: Rederivation and fast-to-compute upper and lower bounds
- Authors: Ricardo Ferreira, Cl\'audia Soares and Marta Guimar\~aes
- Abstract summary: The proliferation of space debris in LEO has become a major concern for the space industry.
The conventional method proposed by Akella and Alfriend in 2000 remains widely used to estimate the probability of collision in short-term encounters.
This study introduces a novel derivation based on first principles that naturally allows for tight and fast upper and lower bounds for the probability of collision.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The proliferation of space debris in LEO has become a major concern for the
space industry. With the growing interest in space exploration, the prediction
of potential collisions between objects in orbit has become a crucial issue. It
is estimated that, in orbit, there are millions of fragments a few millimeters
in size and thousands of inoperative satellites and discarded rocket stages.
Given the high speeds that these fragments can reach, even fragments a few
millimeters in size can cause fractures in a satellite's hull or put a serious
crack in the window of a space shuttle. The conventional method proposed by
Akella and Alfriend in 2000 remains widely used to estimate the probability of
collision in short-term encounters. Given the small period of time, it is
assumed that, during the encounter: (1) trajectories are represented by
straight lines with constant velocity; (2) there is no velocity uncertainty and
the position exhibits a stationary distribution throughout the encounter; and
(3) position uncertainties are independent and represented by Gaussian
distributions. This study introduces a novel derivation based on first
principles that naturally allows for tight and fast upper and lower bounds for
the probability of collision. We tested implementations of both probability and
bound computations with the original and our formulation on a real CDM dataset
used in ESA's Collision Avoidance Challenge. Our approach reduces the
calculation of the probability to two one-dimensional integrals and has the
potential to significantly reduce the processing time compared to the
traditional method, from 80% to nearly real-time.
Related papers
- Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal [46.348283638884425]
This paper introduces a reverse Latent Evolution Model (rLEM) designed for temporal phase of forward beam dynamics.
In this two-step self-supervised deep learning framework, we utilize a Conditional Autoencoder (CVAE) to project 6D space projections of a charged particle beam into a lower-dimensional latent distribution.
We then autoregressively learn the inverse temporal dynamics in the latent space using a Long Short-Term Memory (LSTM) network.
arXiv Detail & Related papers (2024-08-14T23:09:01Z) - Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables [1.9336815376402723]
The increasing volume of space objects in Earth's orbit presents a significant challenge for Space Situational Awareness (SSA)
accurate orbit prediction is crucial to anticipate the position and velocity of space objects, for collision avoidance and space debris mitigation.
We show how the use of machine learning and time-series techniques can produce low positioning errors at a very low computational cost.
arXiv Detail & Related papers (2024-07-03T07:12:33Z) - Inclusive reactions from finite Minkowski spacetime correlation functions [44.99833362998488]
We use real-time methods to determine scattering amplitudes of few-hadron systems for arbitrary kinematics.
In units of the lightest mass of the theory, we find that to constrain amplitudes using real-time methods within $mathcalO(10%)$, the spacetime volumes must satisfy $mL sim mathcalO(10-102)$ and $ mTsim mathcalO(102-104)$.
arXiv Detail & Related papers (2024-06-11T01:39:24Z) - Predicting the Probability of Collision of a Satellite with Space
Debris: A Bayesian Machine Learning Approach [0.0]
Space is becoming more crowded in Low Earth Orbit due to increased space activity.
The need to consider collision avoidance as part of routine operations is evident to satellite operators.
Current procedures rely on the analysis of multiple collision warnings by human analysts.
arXiv Detail & Related papers (2023-11-17T16:41:35Z) - A 5-Point Minimal Solver for Event Camera Relative Motion Estimation [47.45081895021988]
We introduce a novel minimal 5-point solver that estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines.
Our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%.
arXiv Detail & Related papers (2023-09-29T08:30:18Z) - Forecasting Particle Accelerator Interruptions Using Logistic LASSO
Regression [62.997667081978825]
Unforeseen particle accelerator interruptions, also known as interlocks, lead to abrupt operational changes despite being necessary safety measures.
We propose a simple yet powerful binary classification model aiming to forecast such interruptions.
The model is formulated as logistic regression penalized by at least absolute shrinkage and selection operator.
arXiv Detail & Related papers (2023-03-15T23:11:30Z) - Machine Learning in Orbit Estimation: a Survey [1.9336815376402723]
It is estimated that around one million objects larger than one cm are currently orbiting the Earth.
Current approximate physics-based methods have errors in the order of kilometers for seven-day predictions.
We provide an overview of the work in applying Machine Learning for Orbit Determination, Orbit Prediction, and atmospheric density modeling.
arXiv Detail & Related papers (2022-07-19T00:17:27Z) - Gravitational orbits, double-twist mirage, and many-body scars [77.34726150561087]
We explore the implications of stable gravitational orbits around an AdS black hole for the boundary conformal field theory.
The orbits are long-lived states that eventually decay due to gravitational radiation and tunneling.
arXiv Detail & Related papers (2022-04-20T19:18:05Z) - Towards Automated Satellite Conjunction Management with Bayesian Deep
Learning [0.0]
Low Earth orbit is a junkyard of discarded rocket bodies, dead satellites, and millions of pieces of debris from collisions and explosions.
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.
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.
arXiv Detail & Related papers (2020-12-23T02:16:54Z) - Spacecraft Collision Risk Assessment with Probabilistic Programming [0.0]
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.
We build a novel physics-based probabilistic generative model for synthetically generating conjunction data messages.
arXiv Detail & Related papers (2020-12-18T14:26:08Z) - A Spatial-Temporal Attentive Network with Spatial Continuity for
Trajectory Prediction [74.00750936752418]
We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC)
First, spatial-temporal attention mechanism is presented to explore the most useful and important information.
Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
arXiv Detail & Related papers (2020-03-13T04:35:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.