Conjunction Data Messages for Space Collision Behave as a Poisson Process
This work addresses the need for satellite operators to efficiently manage maneuvers by providing timely predictions of collision warnings, though it is incremental as it applies a known statistical method to a specific domain.
The authors tackled the problem of predicting the arrival of space collision warnings (conjunction data messages) by modeling them as a Poisson process, resulting in a Bayesian model that reduced the average prediction error by over 4 hours compared to a baseline on a test set of 50,000 events.
Space debris is a major problem in space exploration. International bodies continuously monitor a large database of orbiting objects and emit warnings in the form of conjunction data messages. An important question for satellite operators is to estimate when fresh information will arrive so that they can react timely but sparingly with satellite maneuvers. We propose a statistical learning model of the message arrival process, allowing us to answer two important questions: (1) Will there be any new message in the next specified time interval? (2) When exactly and with what uncertainty will the next message arrive? The average prediction error for question (2) of our Bayesian Poisson process model is smaller than the baseline in more than 4 hours in a test set of 50k close encounter events.