Naomi Owens-Fahrner, BAE Systems; Joshua Wysack, BAE Systems; Justin Kim, BAE Systems
Keywords: Space Traffic Management (STM), Space Situational Awareness (SSA), Conjunction Assessment, Orbital Slot Allocation, Collision Avoidance, Network Flow Optimization, Integer Programming, Spacecraft Trajectory Optimization
Abstract:
The rapid increase in satellite deployments, particularly in low Earth orbit (LEO) such as Starlink, OneWeb, and China’s Thousand Sails, has led to unprecedented congestion and heightened risks of conjunction events. With the expansion of mega-constellations, ensuring safe and sustainable space operations has become a critical challenge. Effective space traffic management (STM) strategies must be developed to mitigate collision risks and optimize orbital utilization. As commercial, governmental, and defense stakeholders continue to launch satellites, the urgency for structured, scalable processes to manage the increasingly congested LEO environment grows ever more critical. This paper explores advanced methods for planning high-density constellations within already highly congested LEO regions, focusing on minimizing conjunction risk and maximizing space-to-space LEO observations.
A fundamental approach to this problem is the development of a graph-theoretic framework that models satellites and their potential conjunctions with LEO objects as nodes and weighted edges. This structure enables the application of optimization techniques to reduce collision risk while maximizing space-to-space LEO observations. It also supports efficient evaluation of constellation configurations in large-scale, high-density scenarios. This method will be particularly useful for the design of high-density or mega constellations on the order of hundreds to thousands of satellites, ensuring the safety of the entire LEO environment.
A promising technique to optimize the graph is simulated annealing. Simulated annealing provides a scalable method to optimize constellation orbit design by minimizing the likelihood of high-risk conjunctions. By modeling potential interactions with the LEO object population as weighted edges, the algorithm identifies satellite distributions that optimize competing objectives – namely, minimizing conjunction risk while also maximizing LEO coverage. Since the positional uncertainty of LEO objects is highly dependent on their altitude, thus impacting the probability of conjunction, the implemented probability metric models this uncertainty.
This research provides a rigorous mathematical foundation for dense satellite constellation selection in congested environments. This work is flexible and can provide intelligent or low-risk constellation designs, considering many different mission objects such as LEO coverage, communications, or Earth coverage, to name a few. The proposed framework leverages optimization and strategic decision-making tools to address the pressing challenges in modern space operations. Future work may focus on the practical implementation of these models through simulations, aiming to support policy development and international collaboration in space sustainability efforts.
Date of Conference: September 16-19, 2025
Track: Conjunction/RPO