Scheduling for Space Tracking and Heterogeneous Sensor Environments
Abstract
This dissertation draws on the fields of heuristic and meta-heuristic algorithm development, resource allocation problems, and scheduling to address key Air Force problems. The world runs on many schedules. People depend upon them and expect to be accurate. A process is needed where schedules can be dynamically adjusted to allow tasks to be completed efficiently. For example, the Space Surveillance Network relies on a schedule to track objects in space. The schedule must use sensor resources to track as many high-priority satellites as possible to obtain orbit paths and to warn of collision paths. Any collisions that occurred between satellites and other orbiting material could be catastrophic. To address this critical problem domain, this dissertation introduces both a single objective evolutionary tasker algorithm and a multi-objective evolutionary algorithm approach. The aim of both methods is to produce space object tracking schedules to ensure that higher priority objects are appropriately assessed for potential problems. Simulations show that these evolutionary algorithm techniques effectively create schedules to assure that higher priority space objects are tracked.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 16, 2022
- Accession Number
- AD1177673
Entities
People
- Gabriel H. Greve
Organizations
- Air Force Institute of Technology