Decision-theoretic Learning for Tracking in Complex Environments
Abstract
The PI, Dr. Antonia Papandreou-Suppappola, has proposed research in dynamic decision-making for object tracking in any environment and under any conditions. Her approach encompasses a new sense-learn- adapt-infer paradigm within an adaptive and cognitive system framework. The paradigm integrates methodologies from sequential Bayesian filtering, optimization, and artificial intelligence with machine learning. It involves processing of sensor measurements, learning unknown complex environments, adapting transmit parameters, and inferring object state information, thus contributing toward the situational awareness and understanding of the tracking scene, for examples in air, space, land and sea.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 06, 2024
- Source ID
- FA95502310328
Entities
People
- Antonia Papandreou-suppappola
Organizations
- Air Force Office of Scientific Research
- Arizona State University
- United States Air Force