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Completed TRL 2 (started at 2, targeting 3)
Whether adding a single satellite to a train of historical missions, or launching a vast network of CubeSats, the advent of distributed spacecraft missions (DSMs) marks the transition of traditional mission concepts to the modern era. To meet ever-restricting requirements, DSMs offer an avenue to take full advantage of the truly expansive trade space in space-borne remote sensing mission design. DSMs suffer from a combinatorial increase in trade space size. As such, NASA Technology Roadmaps TA 11.2 highlights a need for modern methods for mission analysis and design. In this proposal, we address this need through the fundamental element in trade space analysis for remote sensing missions: coverage simulation. By reducing simulation time, more variables can be traded in less time, increasing trade study fidelity. This is especially necessary for DSMs, with multiple satellites and multiple configurations per satellite. Our objective is to resolve trade space complexity by proposing a novel gridding method for coverage analysis. We present Iterative Distance Gridding (IDG), which will provide much higher speed and sustained accuracy over wide spatial and temporal domains. This is done by determining grid point distance relative to the track rather than determining whether grid points lie within a polygon, an inefficiency of traditional methods. With such performance, we additionally present satellite data product uncertainty (SDPU) as a coverage metric to enhance trade space analysis. Not only do we seek to reduce the computational burden of trade space analysis, we also have the opportunity to enhance such analysis, directly tying together instrument selection and coverage metrics. This inherently supports NASA objectives to modernize DSM analysis tools.
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