Formal Verification of Stochastic State Awareness for Dynamic Data Driven Intelligent Aerospace Systems
Abstract
Future intelligent and autonomous aerospace systems will be able to “feel,” “think,” and “react” in real time based on high resolution ubiquitous sensing enabling self awareness, self diagnostic, and self healing capabilities. Such systems are extremely complex and require the seamless integration of dynamic data, algorithms, computation, and interpretation, i.e. falling within the area of Dynamic Data Driven Application Systems (DDDAS); DDDAS can dynamically incorporate real time data into an executing application, and in reverse, can steer the data measurement processes based on the system s dynamic data integration and interpretation. Towards this end, the main objective of this work is the postulation of a novel stochastic dynamic data driven approach that will enable the next generation of self aware, self diagnostic, and self healing aerospace DDDAS within a formal verification framework that can prove the correctness of stochastic state awareness algorithms with respect to mathematically well defined and precise (formal) specifications of data, models, and system properties.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910054
Entities
People
- Fotios Kopsaftopoulos
Organizations
- Air Force Office of Scientific Research
- Rensselaer Polytechnic Institute
- United States Air Force