Methods for Identifying Cost-efficient Tracking Solutions for Interplanetary Spacecraft

Open Access
Muncks, John P
Graduate Program:
Aerospace Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
December 05, 2014
Committee Members:
  • David Spencer, Thesis Advisor
  • interplanetary spacecraft
  • spacecraft tracking
  • cost-efficient
  • Deep Space Network
In this thesis, a process to identify cost-efficient tracking solutions for interplanetary missions is presented. The process is flexible enough to be used in a wide variety of cases with different constraints. Mission designers will be able to use this process to not only identify the most cost-efficient tracking methods for any particular mission, but also to learn about the characteristics of a cost-efficient solution. In the process, complete tracking solutions are generated, tested, and compared. Tracking solutions combine a tracking schedule, defining when and for how long a spacecraft is tracked, and an antenna configuration, defining which antennas are used. A representation of tracking schedules is used that defines all schedules as a function of the number of tracking intervals and the grouping of these intervals into sets. This representation allowed a wide range of potential solutions to be tested while also being simple enough to offer easy identification of trends in the final data. The process presented in this thesis consists of selecting a range of the variables defining each tracking solution, identifying constraints that limit the choices for these variables, and testing and comparing the efficiency of each of the available solutions. The tracking efficiency, a variable defined in this thesis, is used to compare the solutions. It is a measure of how well the observations of a tracking solution are converted to measurable decreases in the uncertainty in the estimate of the spacecraft’s state. In order to demonstrate the results of this process, a representative lunar trajectory is examined. This demonstration includes the selection of the range of independent variables to test, the specific parameters of the trajectory to be tested, and the simulator that will be used. Several cases, each with different constraints, will be considered in the demonstration. The results of the process allow for quick identification of the most cost-efficient solution. They also show trends in the independent variables that show the sensitivity of cost-efficiency to each of the variables. These trends allow the mission designer to understand the trade-offs in their selection of the variables. By identifying these trends and patterns, more customizable searches can be used to obtain more specific results. The process created an end product that met all of the research goals.