Adaptive Data-Driven Actionable Intelligence for SSA in an Evidential Framework
Abstract
The proposed work spans the development of three core algorithmic platforms: (i.) an adaptive, closed-loop uncertainty forecasting framework that can provide mid-to-long term estimates of quantities of interest critical to conjunction assessment with provable performance guarantees; (ii.) a data-driven operator theoretic modeling framework for building reliable dynamics models for newly detected/suspicious or unusual space objects; (iii.) an evidential sensor fusion framework to account for sensor ambiguity and/or anomalies in multisource data fusion in the SSA problem.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010083
Entities
People
- Mrinal Kumar
Organizations
- Air Force Office of Scientific Research
- Ohio State University
- United States Air Force