What is it about?
The method we propose in this work is a new approach to predicting the position, velocity, and uncertainty in both of a space object in a complex trajectory. What makes our method different is that it strips away many of the assumptions that are taken for granted by most nonlinear filters (algorithms that fuse predicted motion with measurements to get a best estimation). We demonstrate the ability of our method to track objects that are primarily influenced by the gravity of two bodies, i.e. Jupiter and Europa or the Sun and the Earth.
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Why is it important?
Current methods for predicting the state of a space object are dependent on frequent observations of the object. While this is currently feasible, as the number of objects in space grows, this may not be possible in the future. The method we propose is capable of accurately predicting the state and uncertainty of a space object in a complex trajectory for long periods without observations, making it flexible and robust for all kinds of different future mission trajectories.
Perspectives
When examining the history of nonlinear state estimation, especially in the context of astrodynamics, an important consideration is that our algorithms have barely changed since the Space Race of the 20th century. Despite the significant transformation in the entirety of space travel compared to its ancestral counterparts, we are still employing filtering techniques that were invented over 60 years ago for spacecraft estimation. I believe that work like this may ignite inspiration for those in the field of nonlinear estimation, encouraging them to explore new and efficient methods that leverage the technology of our time
Benjamin Hanson
University of California San Diego
Read the Original
This page is a summary of: State Estimation of Chaotic Trajectories: A Higher-Dimensional, Grid-Based, Bayesian Approach to Uncertainty Propagation, January 2024, American Institute of Aeronautics and Astronautics (AIAA),
DOI: 10.2514/6.2024-0426.
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