Conjunction Data Messages behave as a Poisson Process

TL;DR

This paper models the arrival of conjunction data messages as a Poisson process to predict their timing and frequency, aiding satellite operators in timely decision-making with improved accuracy.

Contribution

It introduces a Bayesian Poisson process model to accurately predict the timing of new conjunction data messages, enhancing space debris monitoring strategies.

Findings

Prediction error for message timing is reduced by over 4 hours.

Model outperforms baseline in predicting message arrivals.

Effective for large datasets of close encounter events.

Abstract

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.