Abstract
This work presents a new formulation of the Gaussian particle flow filter derived using an information theoretic approach. The developed information flow addresses two problems with Gaussian particle flow: the lack of inherent meaning in the flow parameters and the inability to easily include modifications for a non-Bayesian update. Equivalency between Gaussian particle flow and information flow is established using a linear, Gaussian example. An orbit determination simulation with high initial uncertainty is used to demonstrate the consistent, robust performance of the information flow filter in situations where the extended Kalman filter fails.
Recommended Citation
K. C. Ward and K. J. Demars, "Information-Based Particle Flow for High Uncertainty Estimation," AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics, Jan 2020.
The definitive version is available at https://doi.org/10.2514/6.2020-1697
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Full Access
International Standard Book Number (ISBN)
978-162410595-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Institute of Aeronautics and Astronautics, All rights reserved.
Publication Date
01 Jan 2020
