AA222_satreconfig

AA 222 Spring 2024 Final Project Repository

By Emmie Catrambone, Michelle Ho, and Kendall Seefried

This study investigates the effectiveness of multiple optimization strategies when tackling the problem of regaining area coverage after losing one or more satellites from a satellite constellation. The robustness of satellite constellation configurations when facing one or more satellite failures is becoming more and more important as space debris that can damage satellites increases in Earth's orbit, among other factors. The method employed in this study initializes a satellite configuration of 20 satellites, propagates that configuration, and calculates the resulting surface area coverage of the Earth throughout the propagation. Then, $m$ satellites are "lost", mimicking multiple system failures, and the remaining satellite altitudes and inclinations are optimized to best return to the original area coverage obtained by the full satellite constellation. Both a genetic Quasi-Newton method and a population method are employed for the optimization; the genetic Quasi-Newton method proves to place the satellites in the best configuration to maximize original coverage, regaining almost 100% of area coverage in every simulation. Both strategies are also compared to a random policy, in which the Quasi-Newton method always performs better than the random policy, and the population method performs just as well as the random policy in most cases. In conclusion, employing strategies to optimally reconfigure satellite constellations in wake of multiple system failures is a viable way to ensure the robustness and performance of these constellations from the perspective of area coverage.