Multidisciplinary design optimization for a high-resolution earth-imaging constellation

Imaging of the Earth's surface is a desired capability for many applications and problem domains. Space-based systems provide a unique platform that can provide persistent coverage over a wide region and have many advantages over atmospheric systems. The case study described here starts with the development of a concise mission statement for an affordable constellation of high-resolution Earth imaging satellites. The problem is modeled using integrated multidisciplinary analysis modules that were validated through simulation and comparison to three known real-world systems. Through multiple techniques, a Pareto Front of non-dominated designs is identified. Two candidate designs, which are on the Pareto front and balance the cost versus performance trade-off, are identified through two different heuristic optimization methods; simulated annealing and genetic algorithms. Key trades are shown to be primarily driven by design variables for optical aperture size, the number of satellites per orbital plane, and the number of orbital planes in the constellation. This can further be shown to create three distinct design regions primarily characterized by the number of satellites in the constellation.