Semester
Summer
Date of Graduation
2026
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
Hang Woon Lee
Committee Member
Andrew Rhodes
Committee Member
Piyush Mehta
Committee Member
Zeyu Liu
Abstract
As interest in cislunar space grows, so does the need for space domain awareness (SDA) capabilities to safely support missions. To enhance cislunar SDA capabilities, the use of space-based observers has been proposed. Prior research has explored optimizing observer placement but has not considered scheduling limitations. I propose a mixed-integer linear programming formulation that concurrently optimizes the placement and scheduling of space-based observers for cislunar SDA. This work considers three aspects of observer scheduling: sensor tasking, communication, and charging. A scenario-based approach is utilized that generalizes observer placement for unknown targets while applying scheduling constraints to each scenario. However, this problem quickly becomes difficult for commercial solvers to handle, even for moderately sized instances. To overcome this, a column generation approach is employed to introduce well-performing schedules into the problem for each orbital slot. A comparative analysis is performed across multiple instances to evaluate the performance of column generation against that of a commercial solver. A case study is then conducted in which the performance of the observers' placement from the concurrent formulation is tested against a validation target set and compared with the performance of two baselines in which placement and scheduling are performed sequentially.
Recommended Citation
Amato, Dominic Kenneth, "Concurrent Space-Based Observer Placement and Scheduling for Cislunar Space Domain Awareness" (2026). Graduate Theses, Dissertations, and Problem Reports (ETD). 13466.
https://researchrepository.wvu.edu/etd/13466
Included in
Navigation, Guidance, Control and Dynamics Commons, Systems Engineering and Multidisciplinary Design Optimization Commons