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

Maximizing the utility of limited Earth observing satellite resources is a difficult ongoing problem. Dynamic Tar- geting is an approach to this challenge that intelligently plans and executes primary sensor observations based on information from a lookahead sensor. However, current implementations have failed to account for realistic satellite operational con- straints and have used static utility for repeat observations of the same target. To address these limitations, we implement a more general Dynamic Targeting framework that comprises a physics-based slew model, a dynamic model of observation utility, and an algorithm for gathering high-utility observations. To demonstrate this framework, we also supply complex dy- namic utility models that are applicable to many missions and new algorithms for intelligently scheduling observations with slewing restrictions and changing utility, including a greedy algorithm and a depth-first search algorithm. To evaluate these algorithms, we test their performance across simulated runs through two datasets and compare to the performance of an algorithm representative of most scheduling algorithms aboard Earth science missions today as well as an intractable upper bound. We show that our algorithms have great potential to improve science return from Earth science missions.