Location: Easton Hub Auditorium
Abstract: Space situational awareness (SSA) concerns understanding the space environment and its influence on active space assets. The proliferation of active space objects and inactive space debris impose great challenges for SSA because the number of space objects of interest is considerably more than the number of sensors available for tracking them. The precise characterization and determination of space object orbits without sensor observations and with large uncertainties after propagation are critical to many SSA functions, such as space object detection, tracking, conjunction analysis, maneuver scheduling, and probability of collision.
This talk presents the recent research on orbit determination and propagation for SSA via two active sampling based surrogate models. Surrogate models have been widely used to replace the original orbital dynamic model by an easy-to-evaluate function model. The first surrogate model for orbital uncertainty propagation is the polynomial chaos based Kriging. The polynomial chaos represents the global trend of the uncertainty distribution while the Kriging describes the local variations. A new active learning based sampling strategy is utilized to incrementally build and improve the model. The second surrogate model is the arbitrary polynomial chaos, which is a data-driven generalization of the conventional polynomial chaos towards arbitrary distribution with arbitrary probability measures. It does not need any assumption on the uncertainty distribution for orbit determination, and avoids the restricted parametric representation of the orbital uncertainty since the polynomial chaos are all determined from the sampling data. The multi-element approach and admissible region are leveraged to further improve the accuracy and computation efficiency of the model for the short-arc and long-term propagation. A high Earth orbit and a low Earth orbit uncertainty propagation examples are presented to demonstrate the effectiveness of the two new surrogate models. Simulations have shown advantageous results compared with the existing methods in the literature.
Bio: Ming Xin is a Professor in the Department of Mechanical and Aerospace Engineering at University of Missouri. He received his B.S. and M.S. degrees from Nanjing University of Aeronautics and Astronautics, Nanjing, China, both in Automatic Control, and his Ph.D. in Aerospace Engineering from Missouri University of Science and Technology. His research interests include flight mechanics, guidance, navigation, and control of aerospace vehicles, optimal control theory and applications, estimation/filtering, and cooperative control of multi-agent systems. Dr. Xin was the recipient of the National Science Foundation CAREER Award. He is an Associate Fellow of AIAA and a Senior Member of IEEE and AAS. He was the Technical Program Chair of 2014 and 2011 AIAA Atmospheric Flight Mechanics Conferences. He is an Associate Editor for AIAA Journal of Spacecraft and Rockets and ASME Journal of Dynamic Systems, Measurement, and Control, and a Technical Editor for IEEE/ASME Transactions on Mechatronics.
For additional information, please contact Professor Qingze Zou at email@example.com or 848-445-4763.