Abstract
A new formulation of the Gaussian particle flow filter is presented using an information theoretic approach. The developed information-based form advances the Gaussian particle flow framework in two ways: it imparts physical meaning to the flow dynamics and provides the ability to easily include modifications for a non-Bayesian update. 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 with Convergence Control," IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 2, pp. 1377 - 1390, Institute of Electrical and Electronics Engineers, Apr 2022.
The definitive version is available at https://doi.org/10.1109/TAES.2021.3123296
Department(s)
Mechanical and Aerospace Engineering
International Standard Serial Number (ISSN)
1557-9603; 0018-9251
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Apr 2022
