Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking

Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking

A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the fil...

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Bibliographic Details
Main Authors: Markus Frohle, Karl Granstrom, Henk Wymeersch
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Gaussian processes
multitarget tracking
posterior fusion
target extent
Online Access:https://ieeexplore.ieee.org/document/9136678/
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spelling doaj-993b882b13fc4db2903ebfe93b32a7ab2021-03-30T02:19:18ZengIEEEIEEE Access2169-35362020-01-01812641412642710.1109/ACCESS.2020.30080079136678Decentralized Poisson Multi-Bernoulli Filtering for Vehicle TrackingMarkus Frohle0https://orcid.org/0000-0002-5274-2933Karl Granstrom1https://orcid.org/0000-0002-3450-988XHenk Wymeersch2https://orcid.org/0000-0002-1298-6159Zenuity, Gothenburg, AB, SwedenEmbark Trucks Inc., San Francisco, CA, USADepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SwedenA decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.https://ieeexplore.ieee.org/document/9136678/Gaussian processesmultitarget trackingposterior fusiontarget extent
collection DOAJ
language English
format Article
sources DOAJ
author Markus Frohle
Karl Granstrom
Henk Wymeersch
spellingShingle Markus Frohle
Karl Granstrom
Henk Wymeersch
Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
IEEE Access
Gaussian processes
multitarget tracking
posterior fusion
target extent
author_facet Markus Frohle
Karl Granstrom
Henk Wymeersch
author_sort Markus Frohle
title Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
title_short Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
title_full Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
title_fullStr Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
title_full_unstemmed Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
title_sort decentralized poisson multi-bernoulli filtering for vehicle tracking
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.
topic Gaussian processes
multitarget tracking
posterior fusion
target extent
url https://ieeexplore.ieee.org/document/9136678/
work_keys_str_mv AT markusfrohle decentralizedpoissonmultibernoullifilteringforvehicletracking
AT karlgranstrom decentralizedpoissonmultibernoullifilteringforvehicletracking
AT henkwymeersch decentralizedpoissonmultibernoullifilteringforvehicletracking
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