Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi...

Descripción completa

Detalles Bibliográficos
Autores principales: Du, Haocui, Xie, Weixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570961/
https://www.ncbi.nlm.nih.gov/pubmed/32962273
http://dx.doi.org/10.3390/s20185387
_version_ 1783597067154751488
author Du, Haocui
Xie, Weixin
author_facet Du, Haocui
Xie, Weixin
author_sort Du, Haocui
collection PubMed
description The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter.
format Online
Article
Text
id pubmed-7570961
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75709612020-10-28 Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter Du, Haocui Xie, Weixin Sensors (Basel) Article The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter. MDPI 2020-09-20 /pmc/articles/PMC7570961/ /pubmed/32962273 http://dx.doi.org/10.3390/s20185387 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Du, Haocui
Xie, Weixin
Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter
title Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter
title_full Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter
title_fullStr Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter
title_full_unstemmed Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter
title_short Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter
title_sort extended target marginal distribution poisson multi-bernoulli mixture filter
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570961/
https://www.ncbi.nlm.nih.gov/pubmed/32962273
http://dx.doi.org/10.3390/s20185387
work_keys_str_mv AT duhaocui extendedtargetmarginaldistributionpoissonmultibernoullimixturefilter
AT xieweixin extendedtargetmarginaldistributionpoissonmultibernoullimixturefilter

Ejemplares similares