Abstract
In light of the ever-increasing value of space assets operating in the presence of a continuously growing population of space objects in orbit about Earth, space situational awareness is becoming more crucial than ever. The process of tracking and maintaining estimates for the numerous assets and space objects is a complex and highly demanding problem, as it requires robust state estimates in the presence of sparse data. As the number of space objects greatly outweighs the number of available sensors, a sensor tasking policy is necessary to properly utilize the available sensor resources in a way that ensures targets are observed regularly in order to maintain state estimates. Previous research has established the efficacy of both myopic and forecasted divergence measures as sensor tasking objectives; however, their implementations can require significant computational effort when generating large sensor schedules. This work studies differing optimization strategies to provide different mentalities of sensor schedule optimization, with either run-time or schedule quality being the emphasis. Properties of the objective functions are leveraged to provide guarantees on the level of optimality for greedy solutions, while a convex closure form is utilized to provide a more robust, albeit slower, optimization approach. The results illustrate a trade-off between performance and schedule generation time and provide a relative comparison between the more computational demanding solutions and their more inexpensive counterparts.
Recommended Citation
M. J. Gualdoni and K. J. Demars, "Optimization Strategies for Myopic and Forecasted Divergence-Based Sensor Tasking Objectives," AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics, Jan 2020.
The definitive version is available at https://doi.org/10.2514/6.2020-0720
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Full Access
International Standard Book Number (ISBN)
978-162410595-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Institute of Aeronautics and Astronautics, All rights reserved.
Publication Date
01 Jan 2020
Comments
U.S. Department of Education, Grant P200A150309