An Implementation of the Poisson Multi-Bernoulli Mixture Trajectory Filter via Dual Decomposition
Paper in proceedings, 2018

This paper proposes an efficient implementation of the Poisson multi-Bernoulli mixture (PMBM) trajectory filter. The proposed implementation performs track-oriented N-scan pruning to limit complexity, and uses dual decomposition to solve the involved multi-frame assignment problem. In contrast to the existing PMBM filter for sets of targets, the PMBM trajectory filter is based on sets of trajectories which ensures that track continuity is formally maintained. The resulting filter is an efficient and scalable approximation to a Bayes optimal multi-target tracking algorithm, and its performance is compared, in a simulation study, to the PMBM target filter, and the delta generalized labelled multi-Bernoulli filter, in terms of state/trajectory estimation error and computational time.

Bayesian estimation

data association

random finite sets

multi-frame assignment

set of trajectories

multiple target tracking

Author

Yuxuan Xia

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

Karl Granström

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

Lennart Svensson

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

Angel Garcia

University of Liverpool

2018 21st International Conference on Information Fusion, FUSION 2018

2453-2460 8455236

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

COPPLAR CampusShuttle cooperative perception & planning platform

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

Subject Categories

Control Engineering

Signal Processing

Computer Science

DOI

10.23919/ICIF.2018.8455236

More information

Latest update

12/6/2019