Multiscan implementation of the trajectory poisson multi-Bernoulli mixture filter
Artikel i vetenskaplig tidskrift, 2019

The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multitarget distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process. This paper considers a recently developed formulation of the multitarget tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established. A multiscan trajectory PMBM filter and a multiscan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multiscan trajectory MBM01 filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented. This paper proposes an efficient implementation that performs track-oriented N-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multiframe assignment problem. The performance of the presented multitarget trackers, applied with an efficient fixed-lag smoothing method, is evaluated in a simulation study.


Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Angel Garcia

University of Liverpool

Jason L. Williams

Queensland University of Technology (QUT)

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Journal of Advances in Information Fusion

1557-6418 (ISSN)

Vol. 14 2 213-235




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