Information theoretic space object data association methods using an adaptive Gaussian sum filter

Richard Linares, Vishwajeet Kumar, Puneet Singla, John L. Crassidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

This paper shows an approach to improve the statistical validity of orbital estimates and uncertainties as well as a method of associating measurements with the correct space objects. The approach involves using an adaptive Gaussian mixture solution to the Fokker-Planck-Kolmogorov equation for its applicability to the space object tracking problem. The Fokker-Planck-Kolmogorov equation describes the timeevolution of the probability density function for nonlinear stochastic systems with Gaussian inputs, which often results in non-Gaussian outputs. The adaptive Gaussian sum filter provides a computationally efficient and accurate solution for this equation, which captures the non-Gaussian behavior associated with these nonlinear stochastic systems. This adaptive filter is designed to be scalable, relatively efficient for solutions of this type, and thus is able to handle the nonlinear effects which are common in the estimation of resident space object orbital states. The main purpose of this paper is to develop a technique for data association based on information theoretic approaches that are compatible with the adaptive Gaussian sum filter. The adaptive filter and corresponding measurement association methods are evaluated using simulated data in realistic scenarios to determine their performance and feasibility.

Original languageEnglish (US)
Title of host publicationSpaceflight Mechanics 2011 - Advances in the Astronautical Sciences
Subtitle of host publicationProceedings of the 21st AAS/AIAA Space Flight Mechanics Meeting
Pages665-680
Number of pages16
Publication statusPublished - Oct 6 2011
Event21st AAS/AIAA Space Flight Mechanics Meeting - New Orleans, LA, United States
Duration: Feb 13 2011Feb 17 2011

Publication series

NameAdvances in the Astronautical Sciences
Volume140
ISSN (Print)0065-3438

Other

Other21st AAS/AIAA Space Flight Mechanics Meeting
CountryUnited States
CityNew Orleans, LA
Period2/13/112/17/11

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All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

Cite this

Linares, R., Kumar, V., Singla, P., & Crassidis, J. L. (2011). Information theoretic space object data association methods using an adaptive Gaussian sum filter. In Spaceflight Mechanics 2011 - Advances in the Astronautical Sciences: Proceedings of the 21st AAS/AIAA Space Flight Mechanics Meeting (pp. 665-680). (Advances in the Astronautical Sciences; Vol. 140).