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Simulation of distributed space missions (DSMs) for the purpose of phase-A mission design studies and general tradespace analysis is computationally challenging owing to the necessity of evaluating thousands of architecture options. Machine learning and evolutionary optimization methods have enabled intelligent search of the architectural tradespace of DSMs, including spacecraft and instrument design specifications. A critical computational bottleneck in evaluating architectures is the ability to rapidly simulate orbits of many DSMs with varying parameters for global earth observation and compare their coverage-related performance. When design variables include heterogeneous payload types and characteristics, orbital characteristics, areas of interest, and user constraints, the parameter space may be in thousands. In this article, we describe the difficulty of coverage calculations for narrow field of view (FOV) and conical FOV sensors, and propose a novel algorithm, called quick search and correction (QSC), to overcome it. We also propose new temporal evaluation metrics to characterize the coverage performance of DSMs, as well as a uniform random sampling technique for fast evaluation of overall performance of DSMs. Performance of the proposed methods and metrics are verified on an example Landsat-derived DSM, showing ~100x improvement in computational speed due to the QSC algorithm and ~10-250x due to the sampling technique.

Fast Methods of Coverage Evaluation for Tradespace Analysis of Constellations