Quantifying and Evaluating the Resilience of Optimized Space Constellations for Fire Detection

Joseph J. Cuhran
Matthew K. Jenkins
Michael J. Walters

Co-authored thesis


With greater emphasis on space operations (both offensive and defensive), greater numbers of spacecraft and space debris in various congested orbits, and greater reliance on space capabilities (communications, navigation and surveillance), resilience has become a priority across the space community. However, there is surprisingly limited extant research and analysis on methods for quantifying the resilience of neither a single satellite, nor an entire constellation. This thesis seeks to address this gap in knowledge and applies versions of a resilience equation on large solution sets of satellite constellations optimized for the fire detection mission. The resilience calculation is composed of the following probabilistic components: avoidance, robustness, recovery, and reconstitution. Research explores how to calculate these components and shows their relationship to total resilience of the constellation, and the relationships to numbers of satellites, numbers of orbital planes and overall acquisition cost. A new method for calculating resilience is proposed which is based solely on performance from simulated data and uses only the components of robustness and recovery. This thesis fills a missing body of knowledge on how to incorporate a quantitative measure of resiliency, together with optimization and simulated mission performance, while conducting space constellation design trades.