Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation
Journal article, 2018

We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multitarget tracking with the standard point target measurements without using probability generating functionals or functional derivatives. We also establish the connection with the δ-generalized labeled multi-Bernoulli (δ -GLMB) filter, showing that a δ-GLMB density represents a multi-Bernoulli mixture with labeled targets so it can be seen as a special case of PMBM. In addition, we propose an implementation for linear/Gaussian dynamic and measurement models and how to efficiently obtain typical estimators in the literature from the PMBM. The PMBM filter is shown to outperform other filters in the literature in a challenging scenario.

multiple target tracking (MTT)

Conjugate priors

multiple hypothesis tracking

random finite sets (RFSs)

Author

Angel Garcia

University of Liverpool

Jason L. Williams

Queensland University of Technology (QUT)

Defence Science and Technology Group

Karl Granström

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN)

Vol. 54 4 1883-1901 8289337

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing

DOI

10.1109/TAES.2018.2805153

More information

Latest update

8/28/2018