Initial Orbit Selection for Prioritized Ground Targets Using Coyote Optimization Algorithm

Satellites are a valuable resource in monitoring the Earth for scientific and military tasks. However, the initial orbital parameters determine the ground track of the satellite and, thus which ground locations can be imaged. For a mission designer, selecting the orbital parameters that will maximize the collection of the desired data is imperative. This work investigates the optimal selection of initial orbital parameters for a satellite to monitor a user-supplied list of prioritized ground locations. The ground locations' priorities decrease for subsequent images of a location as a means of encouraging image diversity and prioritizing more valuable locations. The objective function is the summation of the prioritized images collected. The dynamics of the problem are simulated using General Mission Analysis Tool (GMAT). Using GMAT, a robust framework is created where the dynamics can be easily altered to include (or disregard) any perturbation forces; it is also possible to easily include constraints such as lighting or topography that could prevent an image from being collected. The optimization problem is solved using Coyote Optimization Algorithm (COA). COA is a relatively new metaheuristic with promising potential, and it is compared to the more traditional metaheuristic Particle Swarm Optimization (PSO). The results show that COA performs better than PSO in terms of computational time while finding virtually identical initial orbital parameters. The two primary benefits of this work are the creation of a robust framework for initial orbital parameters for a list of user-supplied prioritized ground locations and introducing COA to this class of problems.