(DURIP) ASTRIANET: TOWARD AN AUTONOMOUS MULTI-MODAL/SPECTRAL AND HYPERTEMPORAL SENSING SYSTEM FOR REMOTE SPACE OBJECT IDENTIFICATION, MONITORING, AND ASSESSMENT
Abstract
Near Earth and Cislunar (NEC) space is vastly unmonitored and the behaviors of anthropogenic space objects (ASOs) within it, not rigorously understood. We are interested in the quantification, monitoring, assessment, and prediction of this ASO population as a whole. Consider NEC as an additional ecosystem to Earth’s lands, oceans, and atmosphere. One way to understand an ecosystem is to classify its constituents. Typically, these constituents are classified in terms of their role within energy cycles, where there are producers, consumers, and decomposers. For NEC orbital space, we need to adjust this classification to be in terms of a constituent’s role in the birth, aging, death, and elimination processes of this ASO community. In other words, we need to determine which objects in this ecosystem (a) produce other space objects, (b) are themselves actively controlled or alive, (c) are dead and no longer working, (d) cause the death of other objects, (e) decompose or degrade other objects, and (f) eliminate or remove other space objects from the population. Rockets and objects that explode are examples of birthing objects. Most ASOs in this ecosystem simply get old and stop working but don’t cease to exist. Some objects may cause the death of another object through a collision. The micrometeoroid and small untrackable debris population degrade and contribute to the decomposition of larger trackable objects. Collisions can be considered as decomposing processes. With the advent of active debris removal, we have objects that eliminate the very existence of certain dead objects from the population. Dead objects can still be detrimental to those that are alive, kind of like zombies. All this is exciting and fascinating but requires sensor data. In order to know something, one must measure it. Because the entire ASO population is unmeasurable as a whole, we cannot make use of descriptive statistics. Instead, we must invoke inferential statistics whereby we measure samples of the ASO population and draw conclusions about the entire population with an associated uncertainty. The larger the set of the whole population we can measure, the less ignorance we will have regarding the same and more improved ability to understand it as a whole. We wish to engage in knowledge creation regarding the NEC orbital space ASO population. Information theory sets up quantitative measures of information and of the capacity of various systems to transmit, store, and process information. One meaningful data-driven question is, “What are the upper bounds on what is possible to achieve with a given information carrying medium (channel)?” We are specifically interested in casting the ASO population quantification via remote sensing problem in an information system and theoretic framework and exploring the generalized mechanism to infer or reconstruct the characteristics of objects which have interacted with measured photons. This proposed research can be summarized by one fundamental question: “What can we know and infer about ASOs that can be extracted from measured photons?” We will answer this fundamental theoretical question in conjunction with experimental research, underwritten and enabled by this proposed DURIP.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 20, 2023
- Source ID
- FA95502210487
Entities
People
- Moriba Jah
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Texas at Austin