The histogram Poisson, lab ele d multi-Bernoulli multi-target tracking filter
Author
dc.contributor.author
Cament Riveros, Leonardo
Author
dc.contributor.author
Correa Villanueva, Javier
Author
dc.contributor.author
Adams, Martin
Author
dc.contributor.author
Pérez Flores, Claudio
Admission date
dc.date.accessioned
2021-01-21T19:19:58Z
Available date
dc.date.available
2021-01-21T19:19:58Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Signal Processing 176 (2020) 107714
es_ES
Identifier
dc.identifier.other
10.1016/j.sigpro.2020.107714
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/178284
Abstract
dc.description.abstract
A Random Finite Set (RFS) based multi-target filter is proposed, which utilizes a labeled Multi-Bernoulli distribution to model the multi-target state, together with a Poisson RFS distribution to model target birth. Referred to as the Poisson Labeled Multi-Bernoulli (PLMB) filter, results show that, in simulated environments, it outperforms the Labeled Multi-Bernoulli (LMB), δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) and Labeled Multi-Bernoulli Mixtures (LMBM) filters under general target birth scenarios. An algorithm based on a histogram of Gibbs samples is also proposed which efficiently generates a posterior labeled Multi-Bernoulli distribution in a simple manner using a histogram of the state-measurement asso- ciations obtained by a Gibbs sampler. The histogram approach is readily applicable to all Multi-Bernoulli based filters and is demonstrated in the form of the Histogram-PLMB (HPLMB) filter.
es_ES
Patrocinador
dc.description.sponsorship
United States Department of Defense
Air Force Office of Scientific Research (AFOSR)
FA9550-17-1-0386
CONICYT/PIA Project
AFB180004
Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
CONICYT FONDECYT
1190979
3180319