Gaussian Mixture PHD Filter for Space Object Tracking

Author

Yang Cheng
Kyle J. DeMars, Missouri University of Science and TechnologyFollow
Carolin Früh
Moriba K. Jah

Abstract

A Gaussian mixture Probability Hypothesis Density (PHD) filter for multiple space object tracking is presented. The PHD filter is a computationally tractable approximate Bayesian multi-object filter based on finite set statistics. The intensity of the Gaussian mixture PHD filter is represented by a variable-size Gaussian mixture, which is propagated and updated by a Gaussian mixture filter that accounts for the nonlinear effect of long-term orbit propagation. A numerical example is used to demonstrate the viability of the filter for space object tracking. © 2013 2013 California Institute of Technology.

Recommended Citation

Y. Cheng et al., "Gaussian Mixture PHD Filter for Space Object Tracking," Advances in the Astronautical Sciences, vol. 148, pp. 649 - 668, Univelt, Inc., Jan 2013.

Department(s)

Mechanical and Aerospace Engineering

International Standard Serial Number (ISSN)

0065-3438

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Univelt, Inc., All rights reserved.

Publication Date

01 Jan 2013