Poisson Multi-Bernoulli Mixture Conjugate Prior for Multiple Extended Target Filtering
Journal article, 2020

This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior for multiple extended object filtering. A Poisson point process is used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture describes the distribution of the targets that have been detected. The prediction and update equations are presented for the standard transition density and measurement likelihood. Both the prediction and the update preserve the PMBM form of the density, and in this sense, the PMBM density is a conjugate prior. However, the unknown data associations lead to an intractably large number of terms in the PMBM density, and approximations are necessary for tractability. A gamma Gaussian inverse Wishart implementation is presented, along with methods to handle the data association problem. A simulation study shows that the extended target PMBM filter performs well in comparison to the extended target \delta-generalized labelled multi-Bernoulli and LMB filters. An experiment with Lidar data illustrates the benefit of tracking both detected and undetected targets.

Extended target tracking

multitarget conjugate prior

random finite sets

multitarget filtering

Poisson point process (PPP)

multi-Bernoulli (MB)

Author

Karl Granström

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

Maryam Fatemi

Zenuity

Lennart Svensson

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

IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN)

Vol. 56 1 208-225 8730493

Subject Categories

Probability Theory and Statistics

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/TAES.2019.2920220

More information

Latest update

3/12/2020