Satellite Constellation Design Tradeoffs Using Multiple-Objective Evolutionary Computation

Abstract

Multiple-objective evolutionary computation provides the satellite constellation designer with an essential optimization tool due to the discontinuous, temporal, and/or nonlinear characteristics of the metrics that architectures are evaluated against. In this work, the nondominated sorting genetic algorithm 2 (NSGA-2) is used to generate sets of constellation designs (Pareto fronts) that show the tradeoff for two pairs of conflicting metrics. The first pair replicates a previously published sparse-coverage tradeoff to establish a baseline for tool development, whereas the second characterizes the conflict between temporal (revisit time) and spatial (image quality) resolution. A thorough parameter analysis is performed on the NSGA-2 for the constellation design problem so that the utility of the approach may be assessed and general guidelines for use established. The approximated Pareto fronts generated for each tradeoff are discussed, and the trends exhibited by the nondominated designs are revealed. Nomenclature a = semimajor axis, km e = eccentricity F = focal length, m i = inclination, deg K = units conversion constant M = mean anomaly, deg P = pixel size, μm e = elevation, deg ρ = range, km � = right ascension of the ascending node, deg ω = argument of perigee, deg