Disaggregated Imaging Spacecraft Constellation Optimization with a Genetic Algorithm

摘要

This research is an extension of work by Major Robert Thompson, who uses a genetic algorithm to optimize certain parameters of a disaggregated constellation for most cost-effective coverage. This work looks at imaging sensor coverage of a specific target deck assumed to exist in the Middle East. Parameters varied in this optimization affect Walker constellation characteristics, orbital elements, and sensor size. Walker parameter variables are number of planes, number of satellites per plane, true anomaly spread, and RAAN increment. All classical orbital elements are variable, although a circular, low-Earth orbit is assumed. Sensor size is varied dependent upon sensor diameter. These parameters are applied to constellations of small satellites and large satellites. The Unmanned Spacecraft Cost Model (USCM) and the Small Spacecraft Cost Model (SSCM) are used to roughly determine the cost of each proposed mission. The sensor effectiveness is determined by the General Imaging Quality Equation (GIQE).