Randomized Greedy Algorithms for Sensor Selection in Large-Scale Satellite Constellations

As both the number and size of satellite constellations continue to increase, there likewise exists a growing need for incorporating methods for autonomous sensor selection into these networks. Particularly, constraints due to computation and communication can often prevent all available satellite sensors from actively making observations at a given time. We pose this constrained sensor selection problem in terms of a submodular optimization problem and explore the use of randomized greedy algorithms to obtain an approximately optimal sensor selection. To this end, we propose a novel pair of randomized greedy algorithms, namely, modified randomized greedy and dual randomized greedy to approximately solve budget and performance-constrained problems, respectively. For each of these algorithms, we derive theoretical high-probability guarantees bounding their suboptimality. We then demonstrate the efficacy of these algorithms in several pertinent applications for Earth-observing constellations, specifically, state estimation for atmospheric weather conditions and ground coverage.