Nonlinear Gaussian Mixture Smoothing for Orbit Determination
Abstract
The forward filtering solution to the Bayesian estimation problem provides the best possible solution for a probability density function given all past and current data. The backward smoothing solution, by contrast, makes use of all data over a fixed interval, through a fixed data lag, or beyond a fixed point in order to determine an improved solution for the probability density function. Achieving a better understanding of the probabilistic description of the state in orbit determination is key to providing reliable situational awareness. This paper investigates a method of combining forward filtering and backward smoothing solutions for non-Gaussian distributions in the orbit determination problem. A simulation of a low-Earth orbit tracking scenario is considered, where a forward filter/backward smoother is applied to assess and compare the performance of filtering and smoothing recursions in a nonlinear, non-Gaussian orbit determination problem.
Recommended Citation
K. J. DeMars, "Nonlinear Gaussian Mixture Smoothing for Orbit Determination," Advances in the Astronautical Sciences, vol. 160, pp. 2117 - 2133, Univelt Inc., Feb 2017.
Meeting Name
27th AAS/AIAA Space Flight Mechanics Meeting (2017: Feb. 5-9, San Antonio, TX)
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Bayesian networks; Filtration; Gaussian distribution; Gaussian noise (electronic); Orbits; Space flight, Backward smoothing; Bayesian estimations; Forward-filtering; Non-gaussian distribution; Nonlinear gaussian; Orbit determination; Probabilistic descriptions; Situational awareness, Probability density function
International Standard Book Number (ISBN)
978-087703637-1
International Standard Serial Number (ISSN)
0065-3438
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Univelt Inc., All rights reserved.
Publication Date
01 Feb 2017
