STATISTICALLY INFERRED MULTI-MODAL PHOTON INFORMATION CONTENT QUANTI*)CATION AND ASSESSMENT VIA QUANTA PHOTOGRAMMETRY
Abstract
Information theory sets up quantitative measures of information and of the capacity of various systems to transmit, store, and process information. One meaningful question is, “What is theoretically knowable about space objects, by measuring their reflected and emitted photons?” We will answer this fundamental theoretical question in conjunction with experimental research. We propose to use Quanta Photogrammetry (QPM) as an optical method that captures and conveys scientific information about a physical object through the process of detecting and mapping single photons reflected off the ASO surface elements in the ASO’s body-fixed reference frame. Because we have direct analogies with Shannon’s Information Systems we expect to be able to use Information Theory in our work, which will yield insights without a loss of generality and thus deliver on basic research goals. If we can quantify our knowledge regarding the physical traits of ASOs in terms of information bits, then we could task data collection sources for information instead of measurements which may not be informative. From a technology perspective, the best we can do is to measure individual photons. Assuming we can achieve this, then the burden of knowledge shifts from technolog constrained to analytics constrained. To wit, the obstacle to knowledge is then in our interpretation of the measured photons. In linearized systems, the Fisher Information Matrix can be mapped via the State Transition Matrix. But looking even more closely, the fundamental issue regarding the existence of information is the presence of differences and/or rates of change! In the absence of differences and/or rates of change, there is exactly zero information content (i.e. no surprisal because we do not observe deviations from our predictions). So we propose to develop some tenets or principles to guide this very important work.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 20, 2023
- Source ID
- FA95502210395
Entities
People
- Moriba Jah
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Texas at Austin