Smoothing for Nonlinear Multi-Target Filters with Gaussian Mixture Approximations
Abstract
This paper investigates a smoothing method using the nonlinear Gaussian mixture probability hypothesis density (GMPHD) filter for use in multi-target tracking. This specific smoother is developed using backwards recursion operations in order to improve upon the preexisting forward filtering solution. The observational and dynamical models considered are nonlinear in nature, creating complexities not present in previous works that developed multi-target smoothers for linear dynamics and measurements. The nonlinear GMPHD smoothing solution is compared to established smoothing solutions to test the validity of the derived algorithms, and Gaussian mixture splitting is implemented to help address common operational problems experienced by the smoother.
Recommended Citation
G. S. Fritsch and K. J. Demars, "Smoothing for Nonlinear Multi-Target Filters with Gaussian Mixture Approximations," Proceedings of the Space Flight Mechanics Meeting (2018, Kissimmee, FL), no. 210009, American Institute of Aeronautics and Astronautics (AIAA), Jan 2018.
The definitive version is available at https://doi.org/10.2514/6.2018-0473
Meeting Name
Space Flight Mechanics Meeting, 2018 (2018: Jan. 8-12, Kissimmee, FL)
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Bandpass filters; Gaussian distribution; Mechanics; Probability density function; Space flight, Dynamical model; Forward-filtering; Gaussian mixtures; Linear dynamics; Multi-target tracking; Nonlinear gaussian; Operational problems; Smoothing methods, Target tracking
International Standard Book Number (ISBN)
978-162410533-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.
Publication Date
01 Jan 2018
