Mission-driven Sensor Network Design for Space Domain Awareness

This research presents a novel framework for optimizing sensor networks to enhance Space Domain Awareness in the face of a burgeoning resident space object population. By employing advanced metaheuristic optimization techniques and high-fidelity modeling and simulation, this research investigates the intricate interplay between sensor characteristics, network topology, and state estimation performance. The research aims to develop actionable recommendations for optimizing sensor network design, considering factors such as viewing geometry, sensor phenomenology, and background noise. Through rigorous simulations and analysis, this work seeks to contribute significantly to the advancement of Space Domain Awareness. A key product of this research is the development of a novel lattice-based genetic algorithm tailored for constrained metaheuristic optimization that converges in 15% fewer generations than traditional methods. This algorithm demonstrates its effectiveness in producing practical sensor network designs that can enhance space object tracking and surveillance capabilities. Results will show network designs that fill current coverage gaps over the Atlantic and Pacific oceans, remain consistent with geographical and geopolitical boundaries, and exploit regions with favorable environmental conditions. The outcome is a set of actionable solutions that triple observation capacity and reduce catalog observation gap times by up to 50%.