Today the US has a tremendous investment in space, especially in military, intelligence, scientific, and commercial sectors. However, one of the most important space vulnerabilities is the lack of persistent situation awareness of the space operational environment to ensure freedom of action. Space can be an important battlefield in modern warfare because intelligence information from space has become extremely vital for strategic decisions. In addition to real-time and hidden information constraints, the presence of adversaries greatly complicates the decision making process. It becomes necessary to perform space defense analysis and mission trade studies. Although pursuit-evasion game theory is relevant to this problem, most results in the existing literature are from the pursuers’ perspective and not applicable.

Innovative solutions are sought for (1) proper game models and constructive game training for a generic space defending scenario where multiple denying assets, defending assets, and pursuing assets with either equal or unequal capabilities are assumed with imperfect, sporadic observations and jamming confrontations; (2) possible constructive methods and approximate solution techniques on distributed learning under sparse communications and adverse environments; (3) efficient computational algorithms to determines real-time cooperative strategies for the space assets, neutral objects, and threats in persistent area denial; and (4) assess the performance under technical failure inaccurate measurements and loss of communications. Proposed advances--together with potential deliverables including novel mathematical developments, interaction modeling, performance metrics, advanced engagement concepts, and design principles--set the foundations to enable assured operations of teams of autonomous defense systems to adapt to hostile, nontraditional environments, which capitalize on effective utilization of modeling and analysis of uncertain systems, as well as multilevel, multi-group, multi-agent, control and decision analysis.

 

References

Shen D, Pham KD, et al: “Pursuit-Evasion Orbital Game for Satellite Interception and Collision Avoidance,” SPIE Defense and Security 2011: Sensors and Systems for Space Applications IV, Proceedings of SPIE 8044: Orlando, FL, 2011

Pham KD: “Risk-Averse Based Paradigms for Uncertainty Forecast and Management in Differential Games of Persistent Disruptions and Denials,” Proceedings of American Control Conference: 842, Baltimore, MD, 2010

 

key words

Active and distributed learning; Modeling of complex systems; Competitive decision making; Adversarial systems; Distributed computation; Multilevel command and control in hostile environment;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants