Poisson Multi-Bernoulli Mixture Trackers: Continuity Through Random Finite Sets of Trajectories
Paper i proceeding, 2018

The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target distribution for which the prediction and update are closed. It has a Poisson birth process, and new Bernoulli components are generated on each new measurement as a part of the Bayesian measurement update. The PMBM filter is similar to the multiple hypothesis tracker (MHT), but seemingly does not provide explicit continuity between time steps. This paper considers a recently developed formulation of the multi-target tracking problem as a random finite set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM trackers. The PMBM trackers efficiently estimate the set of trajectories, and share hypothesis structure with the PMBM filter. By showing that the prediction and update in the PMBM filter can be viewed as an efficient method for calculating the time marginals of the RFS of trajectories, continuity in the same sense as MHT is established for the PMBM filter.

smoothing

trajectories

random finite sets

tracking

data association

filtering

Författare

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Jason Williams

Defence Science and Technology Group

Ángel F. García-Femández

University of Liverpool

2018 21st International Conference on Information Fusion, FUSION 2018

973-981 8455849

21st International Conference on Information Fusion, FUSION 2018
Cambridge, United Kingdom,

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA, 2016-01-01 -- 2018-12-31.

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Signalbehandling

DOI

10.23919/ICIF.2018.8455849

Mer information

Senast uppdaterat

2019-05-14