Dynamic Targeting of Satellite Observations Incorporating Slewing Costs and Complex Observation Utility

Maximizing the utility of limited Earth observingsatellite resources is a difficult ongoing problem. Dynamic Targetingis an approach to this challenge that intelligently plansand executes primary sensor observations based on informationfrom a lookahead sensor. However, current implementationshave failed to account for realistic satellite operational constraintsand have used static utility for repeat observations ofthe same target. To address these limitations, we implementa more general Dynamic Targeting framework that comprisesa physics-based slew model, a dynamic model of observationutility, and an algorithm for gathering high-utility observations.To demonstrate this framework, we also supply complex dynamicutility models that are applicable to many missions andnew algorithms for intelligently scheduling observations withslewing restrictions and changing utility, including a greedyalgorithm and a depth-first search algorithm. To evaluate thesealgorithms, we test their performance across simulated runsthrough two datasets and compare to the performance of analgorithm representative of most scheduling algorithms aboardEarth science missions today as well as an intractable upperbound. We show that our algorithms have great potential toimprove science return from Earth science missions.