Planning with Imperfect Information: Interceptor Assignment
Abstract
The author considers the problem of assigning a scarce number of interceptors to a wave of incoming atmospheric re-entry vehicles (RVs). In this single wave, there is time to assign interceptors to a wave of incoming RVs, gain information on the intercept status, and then, if necessary, assign interceptors once more. However, the status information of these RVs may not be reliable. This problem becomes challenging when considering the small inventory of interceptors, imperfect information from sensors, and the possibility of future waves of RVs. This work formulates the problem as a partially observable Markov decision process (POMDP) to account for the uncertainty in information. The author uses a POMDP solution algorithm to find an optimal policy for assigning interceptors to RVs in a single wave. Prom there, three cases are compared in a simulation of a single wave. These cases are perfect information from sensors; imperfect information from sensors, but acting as if it were perfect; and accounting for imperfect information from sensors using the POMDP formulation. Using a variety of parameter variation tests, he examines the performance of the POMDP formulation by comparing the probability of an incoming RV avoiding intercept and the interceptor inventory remaining. He varies the reliability of the sensors as well as the number of interceptors in the inventory and the number of incoming RVs in the wave. Results show that the POMDP formulation consistently provides a policy that conserves more interceptors and approaches the probability of intercept of the other cases. However, situations do exist in which the POMDP formulation produces a policy that performs less effectively than a strategy assuming perfect information.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2006
- Accession Number
- ADA452253
Entities
People
- Daniel B. Mcallister