THURSDAY, OCTOBER 8TH, 2026

ARMS B061 12:00PM-1:00PM

 

Abstract:

 

Nonlinear estimation is at the heart of spacecraft navigation, yet strongly nonlinear dynamics, non-Gaussian uncertainties, and imperfect models continue to challenge conventional filtering techniques. This seminar presents recent advances that rethink different components of the estimation problem. Higher-order polynomial updates extend the classical Kalman measurement update beyond linear estimators, while polynomial expansions, Gaussian mixtures, Koopman operators, and particle-flow methods provide new ways to propagate and represent uncertainty in nonlinear systems. These developments are then brought together in the challenging context of autonomous active debris removal. Rendezvous with a non-cooperative object requires a spacecraft to estimate not only relative position and attitude, but also uncertain physical properties and changing measurement characteristics. Through adaptive pose estimation, online parameter identification, and tightly coupled estimation and guidance, the seminar illustrates how advances in nonlinear estimation can translate into safer and more autonomous spacecraft operations. 

 

Biography:

 

Dr. Simone Servadio is an Assistant Professor in the Department of Aerospace Engineering at Iowa State University, where he directs the Spacecraft Procedures for Autonomous Control and Estimation (SPACE) Laboratory. His research focuses on astrodynamics, spacecraft guidance, navigation and control, uncertainty quantification, and autonomous space systems. His work develops advanced methods for state estimation, uncertainty propagation, space domain awareness, and spacecraft autonomy, with particular emphasis on nonlinear and non-Gaussian systems and cislunar applications. Before joining Iowa State University, Dr. Servadio was a postdoctoral researcher at MIT’s Astrodynamics, Space Robotics, and Controls Laboratory (ARCLab) working on Koopman Operator theory and orbit capacity modelling. Prior to that, he received his Ph.D. degree in Aerospace Engineering at The University of Texas at Austin, where he worked on the nonlinear filtering problem. He received his B.S degree in Aerospace Engineering and M.S degree in Space Engineering from the Politecnico di Milano.