Discovery of Attack-Resistant Satellite Constellation Configurations via Simulation-Based Evolutionary Computing

Abstract

For decades, outer space was considered a geopolitical sanctuary where spacecraft faced only natural threats. A current trend in space mission planning envisions constellations with hundreds or even thousands of satellites. Today, there is a precedent for satellites to be considered legitimate military targets. While large constellation sizes reduce the mission risk posed by the loss of a single satellite, the worldwide proliferation of offensive cyber capabilities, coupled with our increasing reliance upon space systems, creates a strong threat even against these large constellations. The type of attack addressed in this research is a ground-based, RF-enabled cyberattack (e.g., a spoofed commanding signal) that can take an individual satellite out of mission-capable status. Such an attack, carried out intelligently by a capable adversary, could effectively blind a constellation to events on Earth or disrupt its service to a particular location. A method for defending against such an attack is desired. We propose the concept of a “schooling maneuver” (SM), wherein the satellites in the constellation use propulsive maneuvers to change their relative geometry, presenting a ground-based attacker with a poorer sight picture. This reconfiguration decreases the attacker’s physical access to the constellation, thereby reducing the amount of mission degradation induced by the attacks. We present a method for discovering such maneuvers given the attacker location, the reference constellation configuration, and a do-not-exceed ∆V limit. For a representative example, we demonstrate an evolutionary algorithm that discovers the best SM available within the ∆V limit and validate its performance using a brute-force search of the same configuration space.