Mission set analysis tool for assessing future demands on NASA's Deep Space Network

Abstract

NASA's future scientific missions will place new demands on its deep space network (DSN). Depending on which missions fly and their particular design, NASA's communications ground assets (antennas) may require upgrade or enhancement. The purpose of the mission set analysis tool is to help forecast future demand by (1) cataloguing the characteristics of potential future DSN-user missions consistent with NASA's space communications mission model, (2) calculating the demands that these missions will place on the DSN, and (3) generating mission requirements for other DSN architectural analysis tools. This tool takes the information associated with the Space Communications Mission Model (SCMM) to the next level - using an Microsoft EXCEL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sup> -based database to catalog and process some 300 mission parameters. These parameters fall into one of five broad categories: mission identity and affiliation, operational schedule, communication system capabilities, data rates and ground tracking schedules. Because NASA's mission forecast changes frequently, analysis templates are used to select sets of missions from the database for analysis. Communication link budgets are calculated for particular dates during mission operation based on mission parameters and automated ephemeris algorithms. The result is expressed in terms of the receiving area on the ground required to close each particular communication link. Plots of this receiving area as a function of time and frequency band reveal future demand for different mission sets. The most demanding individual communication links are automatically identified. Radio-frequency (RF) uplinks (Earth to mission) and downlinks (mission to Earth) are treated separately. The Mission Set Analysis Tool also creates mission requirements used by other analysis tools to assess the cost, operability (link performance), and load capacity of proposed DSN architectures.