BAYESIAN NONPARAMETRIC MACHINE LEARNING IN MULTIMODAL SENSING
Abstract
The proposed work investigates additional challenges in the multiple object tracking problem within the Bayesian nonparametric learning framework. In particular, methodologies are developed to learn information from different types of dependencies, to adapt tracking performance to time-varying changes in operational or environmental conditions and transfer knowledge previously learned; and to optimally design transmit waveforms by iteratively leaning from new information as it becomes available.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010132
Entities
People
- Antonia Papandreou-suppappola
Organizations
- Air Force Office of Scientific Research
- Arizona State University
- United States Air Force