Seminars

Abstract

Distributed spacecraft missions (DSMs), such as constellations, offer significant advantages for spaceborne remote sensing applications, such as higher spatial coverage and shorter revisit intervals, key features in Earth exploration and resource exploitation. The problem is that the iterative process of design increases in complexity when many, potentially heterogeneous spacecraft are considered in the trade space. DSMs have the potential to revolutionize Earth observation through increased spatial, temporal, spectral, and radiometric data resolution, but iterating over all possible mission architectures to identify a “best” candidate presents a novel computational challenge. Dominant elements in the trade space are orbit design, instrument choice, the number of satellites, and the DSM (or constellation) structure, each of which may have multiple options, which increases trade space size combinatorically. Compounding this complexity is the need to satisfy multiple objectives at once, balancing total and consistent coverage. Evolutionary algorithms can search the trade space intelligently, but they require many simulation evaluations to approximate the typically multi-objective pareto front. We have developed a semi-analytic routine to compute access times, relaxing the need for repeat ground tracks, simplified constellation structures, or circular orbit assumptions commonly found in literature. In addition, we have formalized observation of probabilistic targets, particularly useful for sub-sampled coverage, as applied to lidar or small swath coverage, or coverage of geophysically obscured targets, such as under cloud cover or dense vegetation. Lower perevaluation runtime coupled with data-driven coverage give mission planners a tool to meet the computational demands of DSM design, exploring the wide array of options to meet Earth science decadal survey measurement objectives.