Space Mission Scheduling Toolkit for Long-Term Deep Space Network Loading Analyses and Strategic Planning

Abstract

The Jet Propulsion Laboratory (JPL) owns and operates the Deep Space Network (DSN), a set of antennas placed around Earth to communicate with spacecraft flying anywhere in the Solar System. While the DSN is a critical asset to JPL and NASA's success, it is also expensive to build, maintain and operate. Therefore, additional system capabilities are planned strategically, years in advance, by forecasting which missions will utilize the system in the coming decades (and their driving data requirements). Then, loading analyses are conducted assuming different scenarios, each one simulating DSN operations for several years.

Within this context, this thesis focuses on developing an automated long-term scheduling mechanism that can mimic real DSN operations. Several factors are modeled and accounted for in this process: Spacecraft visibility constraints, evolution of the DSN architecture, characteristics of each antenna, as well as link and other operational constraints.

To implement the scheduling mechanisms, several options are first identified and downselected. Then, it is explained in detail how the automated long-term scheduling toolkit –LTST– formulates the problem as a mixed