Multiple Set Filtering using Probability Hypothesis Densities
Abstract
Emerging tools derived from finite set statistics model candidate targets as a random finite set (RFS) and use Bayesian inference to produce estimates of the RFS given collected data. This work aims to extend that concept to estimating multiple RFSs simultaneously such that targets belonging to different state spaces may be tracked in the same framework. The new filter utilizes the probability hypothesis densities of the RFS to approximate the joint multitarget Bayes filter of the sets. Expressions for the time and measurement update steps are derived, and modifications are made to enable practical implementation. Numerical studies are presented to illustrate the performance of the filter and it is compared to a representative analog of an existing filter found in related literature.
Recommended Citation
J. S. McCabe and K. J. DeMars, "Multiple Set Filtering using Probability Hypothesis Densities," Advances in the Astronautical Sciences, vol. 167, pp. 2257 - 2276, Springer, Jan 2018.
Department(s)
Mechanical and Aerospace Engineering
International Standard Book Number (ISBN)
978-087703657-9
International Standard Serial Number (ISSN)
0065-3438
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2018
