Peter McGill, Lawrence Livermore National Laboratory; Robert Armstrong, Lawrence Livermore National Laboratory; Alex Geringer-Sameth, Lawrence Livermore National Laboratory; Nathan Golovich, Lawrence Livermore National Laboratory; Trier Mortlock, Lawrence Livermore National Laboratory
https://doi.org/10.64861/WLOO6910
Keywords: space situational awareness, object tracking, sensor scheduling
Abstract:
The number of Resident Space Objects (RSOs) in geostationary orbit (GEO) is growing exponentially. Given GEO’s importance for communications, navigation, and Earth monitoring, the ability to track the GEO RSO population is a key Space Situational Awareness capability. With the advent of low-cost, commercial off-the-shelf small satellite platforms, one approach to tracking GEO RSOs is to use a constellation of space-based sensors. Given an RSO tracking objective, this new possibility raises some important questions: How should the constellation be designed? How many sensors are needed? What orbits should they occupy?
To help answer these questions, we present the Python package slewpy. slewpy allows users to specify an RSO population with observing priorities and a constellation configuration (that is, orbits and satellite parameters). Given these inputs, slewpy can be used to run a time-resolved simulation of an observing mission and outputs simulated target observations, given constraints such as satellite slewing rates, required observing time on a given target, and Sun, Earth, and Moon limb observing constraints. The output of the simulation is a time-resolved log of what RSO was observed and when and by which sensor. Amongst other metrics, this allows a user to test a given constellation configuration against coverage requirements (the total number of RSOs tracked and discovered) and custody requirements (how frequently a given RSO is observed). Ultimately, this allows a user to iterate on constellation design towards more optimal configurations given their specified objectives.
slewpy is built within a discrete event simulation framework, which has the key advantage of only simulating changes at event times. Example events are when an RSO becomes visible, when a space sensor observes an RSO, or when a sensor becomes available after observing an RSO. This approach makes slewpy more computationally efficient than time-step simulations, allowing the simulation of realistic scenarios involving many sensors and thousands of RSOs in GEO orbits. We present results that use slewpy to find effective constellation configurations to track the population of GEO RSOs.
Date of Conference: September 15-18, 2026
Track: Space-Based Assets