Abstract
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.
Highlights
T HE MATURITY of the small satellites and launch access to space over the last two decades has made distributed space missions (DSMs) for earth observation (EO) economically practicable [1]
We explore the possibility of quantifying the performance of a DSM by conducting numerical simulations of the orbit and coverage (O&C) at randomly chosen small intervals within the mission duration, instead of over the entire mission duration
We propose and demonstrate two methods, the quick search and correction (QSC) algorithm and the random sampling method which can be used to either independently or together in DSM evaluations
Summary
T HE MATURITY of the small satellites and launch access to space over the last two decades has made distributed space missions (DSMs) for earth observation (EO) economically practicable [1]. This component is the most time consuming, computationally expensive part of the tradespace analysis owing to the large number of options for orbital specifications, satellite numbers, and geometry, payload characteristics, as well as large number of discrete points on the ground regions and discrete time steps in numerical propagation and coverage computation It significantly slows down heuristic search or optimization techniques applied to DSM design in early formulation. In the section, small errors (missed access events) are analyzed Another critical aspect of tradespace analysis of DSMs is the identification of suitable performance metrics, which provide comparative utility of EO observations across DSM architectures while being computationally efficient. Numerical simulations propagate orbits and compute coverage for all satellites in the DSM, evaluated using fixed time steps over the entire mission lifetime, and aggregate coverage metrics across every possible observation. ORBIT PROPAGATION AND COVERAGE COMPUTATION FOR NARROW FOV AND CONICAL FOV SENSORS
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