Minimizing the Cost of Availability of Coverage from a Constellation of Satellites: Evaluation of Optimization Methods

Satellite systems to provide terrestrial surface coverage are currently optimized by independently optimizing parts of the system or by locally starting at a “baseline ” configuration and making small changes. Even when using a “baseline ” the various components such as constellation configuration, satellite design, and acquisition have generally been optimized separately. A model was developed to minimize the life-cycle cost of the system as a whole with the appropriate linkages between the major parts of the system in both cost and performance. The model consists of a cost model, and a performance model. The cost model includes the cost of purchasing and maintaining an inventory of satellites and launch vehicles, launch operations, and orbit operations. The performance model evaluates the availability over geographic grid averaging areas over a given period of time. The performance of local optimization techniques (Simplex and Complex) was evaluated against global optimization techniques (Simulated Annealing and Genetic Algorithms) for providing availability of coverage. The global optimization techniques performed much better with Genetic Algorithms performing slightly better than Simulated Annealing. The quality of the solutions found was benchmarked against a trade study and local optimization methods, and our proposed global systems approach provided significant cost savings over the existing techniques.