Finite Set Statistics on Manifolds for Space Object Detection, Tracking, Identification and Characterization
Abstract
This work sets out to develop a Finite Set Statistics (FISST) methodology on non-Euclidean manifolds for the multiple resident space object (RSO) detection, tracking, identification, and characterization (DTIC) problem. More specifically, the proposed research will consider the following technical challenges: (1) How to expand the existing FISST theory to include the use of non-Euclidean coordinates, and (2) How to model uncertainty for non-Euclidean coordinates.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 23, 2016
- Source ID
- FA95501610099
Entities
People
- John T. Kent
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Leeds