Local Orbital Elements and Directional Statistics for Cislunar Space Situational Awareness

Dr. Beeson, Ryne

Assistant Professor
Department of Mechanical and Aerospace Engineering
Princeton University

Seminar Information

Seminar Series
Dynamic Systems & Controls

Seminar Date - Time
March 15, 2024, 3:00 pm
-
4 PM

Seminar Location
EBU II 479, Von Karman-Penner Seminar Room

Dr. Beeson, Ryne

Abstract

The past year and a half has been marked with an increasing cadence of exciting space missions by both nation states and private actors toward the Moon and greater cislunar domain.  With the manifest for cislunar missions only increasing in the years ahead, questions that haven't yet needed to be addressed outside of Earth-bound space missions are suddenly relevant and pressing.  In this talk, we address one of these questions; how agencies such as the United States Space Force might conduct efficient space situational awareness (SSA).  Unlike the case of SSA in low Earth orbits, the cislunar domain has strikingly rich dynamics and limited passive and weak observations, making essential steps of SSA, which includes uncertainty propagation, much more challenging.  We present in this talk a formulation that leverages dynamical systems theory to generate local orbital elements upon which probability distributions can be more naturally defined and demonstrate how doing so helps to improve the accuracy, efficiency, and realism of uncertainty propagation in the cislunar domain.

Speaker Bio

Ryne Beeson is an Assistant Professor of Mechanical and Aerospace Engineering at Princeton University.  He received a Ph.D. in Aerospace Engineering and M.S. in Mathematics from the University of Illinois at Urbana-Champaign (2020) and also holds an M.S. and B.S. in Aerospace Engineering from the same institution.  Prior to starting at Princeton University in 2021, he was a Senior Scientist for CU Aerospace (CUA) L.L.C. (2016-2021) in Champaign, Illinois.  He was PI for several NASA Phase I and II SBIRs that developed astrodynamics related software, including parallel (distributed and shared memory) nonlinear programming solvers, and an automated global trajectory optimization solver for multibody environments (pydylan), which is now actively developed at Princeton.  Besides problems in space flight, he is also interested in data assimilation for high dimensional chaotic systems, motivated by weather and climate problems in the geosciences and more recently space weather.